In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

Pediatric interventional cardiology procedures are essential in diagnosing and treating congenital heart disease in children; however, they raise concerns about potential radiation exposure. Managing radiation doses and assessing image quality in angiographs becomes imperative for safe and effective interventions. This systematic review aims to comprehensively analyze the current understanding of physical image quality metrics relevant for characterizing X-ray systems used in fluoroscopy-guided pediatric cardiac interventional procedures, considering the main factors reported in the literature that influence this outcome. A search in Scopus and Web of Science, using relevant keywords and inclusion/exclusion criteria, yielded fourteen relevant articles published between 2000 and 2022. The physical image quality metrics reported were noise, signal-to-noise ratio, contrast, contrast-to-noise ratio, and high contrast spatial resolution. Various factors influencing image quality were investigated, such as polymethyl methacrylate thickness (often used to simulate water equivalent tissue thickness), operation mode, anti-scatter grid presence and tube voltage. Objective evaluations using these metrics ensure impartial assessments for main factors affecting image quality, improving in the characterization fluoroscopic X-ray systems, and aiding informed decisions to safeguard pediatric patients during procedures.

Obturator herniæ are a quite rare type of abdominal hernia, accounting for less then 1% of all abdominal wall hernias and for 0.2-1.6% of patient with a small bowel obstruction. The obturator canal is the only portion of the obturator foramen that is not obliterated by the membrane of the same name and hosts the obturator neurovascular bundle; it is, for this reason, a locus minoris resistentiae, through which obturator hernias are formed, generally in elderly, multiparous women. Despite the fact that Computed Tomography offers the possibility of a quick, correct preoperative diagnosis, often signs and symptoms of obturator herniæ are quite unspecific; considering that, at the same time, patients with an obturator hernia are generally elderly, with concurrent conditions, this might cause delays in the diagnosis and treatment and produces the high postoperative mortality rates that obturator herniæ yield

Retroaortic left renal veins are an often overlooked, but no so rare anatomic anomaly that might cause a few aspecific symptoms and signs, especially flank or inguinal pain, microscopic or even gross hematuria; in the vast majority of cases it is, thought, totally asymptomatic. The nutcracker syndrome that might arise in the case of RLRV is mainly due to the re- duced space between aorta and the vertebral body and the consequent compression on the venal rein; this might in turn cause upstream hypertension, hematuria, varicocele, pain. The compression of RLRV leads to hematuria because of renal venous hypertension, consequent left renal congestion. This upstream venous congestion might result, in some cases, in left-sided varicocele in men and pelvic congestion syndrome in women. From a radiologic point of view, the presence of RLRV and the precise reporting its subtypes is mandatory.

Estimating age at death is essential to establish a biological profile from human skeletal remains in both forensic and archeological settings. Imaging studies of skeletal age changes in adults have described metamorphosis of trabecular bone structure and bone loss in the proximal femur , as well as changes in morphology during different stages of life. This study aims to assess the utility of a digital representation of conventional X-ray films of the proximal femur for the estimation of age at death in a sample of 91 adult individuals (47 females and 44 males) of the Coimbra Identified Skeletal Collection. The proposed approach showed a root mean squared error (RMSE) of 17.32 years (and mean absolute error of 13.47 years) for females and an RMSE of 14.06 years (mean absolute error of 11.08 years) for males. The main advantage of this approach is the consistency in the feature detection and extraction, as X-ray images projected on the femora space will always produce the same set features to be analyzed for age estimation while more traditional methods rely heavily on operator experience which can lead to inconsistent age estimates among experts.

Abstract: Background: Coronary angiography CT scans play a pivotal role in diagnosing cardiovascular diseases, providing crucial information for treatment planning. However, concerns regarding radiation exposure have prompted the need for establishing region-specific diagnostic reference levels (DRLs) to ensure patient safety. This study aims to assess radiation exposure during coronary angiography CT scans in the Northeast Assam population and establish DRLs tailored to this demographic. Methods: A total of 380 patients were referred to the Primus Diagnostic Centre and Heath City Hospital, Guwahati Assam, during the study with coronary artery disturbances. Data on the technical parameters used in CT procedures were taken in 2021-2022. The aim and study's objective was Organ and surface dose to specific radiosensitive organs (Chest) estimation using software imPACT 1.0.4 from National Radiological Protection Board (NRPB) SR250 Monte Carlo dataset. Results: The study population (n = 380) comprised 190 men and 190 women with an average age of 29 to 75 years. The Mean ± SD of BMI and ED are 22.42±1.06 and 21.57±4.27 respectively. The mean DLP is 854.67, and the mean ED is 21.57. The effective doses for males (13- 27) mSv were in females (13-29) mSv. The DRL for Male population was found to be 24.26mSv.cm2 whereas for female population it was 24.69mSv.cm2. Conclusions: This study highlights the necessity of establishing tailored diagnostic reference levels for coronary angiography CT scans in the Northeast Assam population. By doing so, healthcare providers can ensure optimal image quality while minimizing radiation exposure, ultimately enhancing patient safety and quality of care. These findings have implications for radiological practice in the region and contribute to the ongoing efforts to standardize radiation doses in medical imaging procedures.

The aim was to assess the precision and accuracy of cephalometric analyses performed by artificial intelligence (AI) with and without human augmentation. Four dental professionals with varying experience levels and AI identified 31 landmarks on 30 cephalometric radiographs twice. These landmarks were re-identified by all examiners with the aid of AI. Precision and accuracy were assessed by using intraclass correlation coefficients (ICCs) and mean absolute errors (MAEs). AI revealed the highest precision with a mean ICC of 0.97, while the dental student had the lowest (mean ICC: 0.77). AI/human augmentation method significantly improved precision of the orthodontist, resident, dentist, and dental student by 3.26%, 2.17%, 19.75%, and 23.38%, respectively. The orthodontist demonstrated the highest accuracy with a MAE of 1.57 mm/˚. AI/human augmentation method improved the accuracy of the orthodontist, resident, dentist, and dental student by 12.74%, 19.10%, 35.69%, and 33.96%, respectively. AI demonstrated excellent precision and good accuracy in automated cephalometric analysis. The precision and accuracy of the examiners with the aid of AI improved by 10.47% and 27.27%, respectively. The AI/human augmentation method significantly improved the precision and accuracy of less experienced dental professionals to that of an experienced orthodontist.

Purpose: We assessed if a CXR AI algorithm can detect missed or mislabeled chest radiographs (CXRs) findings in radiology reports. Methods: We queried multi-institutional radiology reports search database of 13-million reports to identify all CXRs reports with addendums from 1999-2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addendum was created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports with addendum (279 patients) with errors related to side-discrepancy or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of AI to detect missed or mislabeled findings. Results: AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). Conclusion: The CXR AI could accurately detect mislabeled and missed findings. Clinical Relevance: The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings.

Introduction: Acute rhinosinusitis (ASR) is a common condition in children, which can be burdened by orbital complication (OC) or intracranial complications (IC) that are rare but have potential for serious sequelae; therefore, it needs prompt diagnosis and treatment. The first line of treatment is intravenous antibiotic therapy. In case of failure of clinical improvement after 48 hours, hypovisus or negative prognostic factors (intracranial abscess, large orbital abscess or in intraconic or lateral localization, cavernous sinus thrombosis), surgical treatment is indicated. Objective: the purpose of this work is to describe the clinical profile, treatments and outcome of a series of patients undergoing surgical treatment for complications of ARS at a tertiary referral center. Material and methods: a retrospective analysis of clinical databases was conducted to identify all patients under 16 years of age who underwent surgical treatment for complications of ARS during the 20-year period 2000-2023. Demographic characteristics, clinical data, radiological investigations, surgical treatment, and follow-up performed were reported for each case. Results: 29 patients (20 males and 9 females), mean age 13 years, were included, 69% of whom had OC, 21% had IC, and 7% had both OC and IC. The main symptoms of OC were orbital edema, hypovisus and diplopia; while the main symptoms of IC were headache, fever, comitial seizures and focal neurological symptoms. Mean hospitalization time was 11.75 (± 9.2) days. The mean length of hospital stay (LOS) was 9.1 days for OC, 21 days for IC and 14 days for OC + IC. A single patient manifested a Potts Puffy Tumor, with a LOS of 10 days. Basal CT and gadolinium-enhanced MRI were performed in all cases. Intravenous antibiotic therapy and surgical clearance was undertaken. The majority of patients (76%) underwent endoscopic endonasal approach, 24% external transorbital approach, 1,16% orbital decompression and 0,58% superior eyelid approach. Any complications related to the surgical procedures were found. All patients with hypovisus and diplopia had complete recovery. During follow-up 7 patients (24%) had sinus recurrences, at a time range of 1 to 4 years after surgery. Conclusion: our experience, in agreement with the data reported in the literature, confirmed the importance of appropriate diagnostic-therapeutic management to avoid potentially serious sequelae of complicated ARS.

The aim of this study was to evaluate the quality of legends associated with diagnostic images in the published oral and maxillofacial radiology literature using a novel rating scale. Images and their corresponding legends were randomly selected from published manuscripts over the last ten years in the Oral Radiology journals, namely Dentomaxillofacial Radiology, Imaging Science in Dentistry, Oral Radiology, and Oral Surgery Oral Medicine Oral Pathology Oral Radiology. An Image Quality Rating Scale (IQRS) was introduced to assess the quality of the legends associated with images. A program was developed for the rating scale form using Google Apps Script API to gather and analyze the data. The rating scale ranged from 1 to 5, with 5 as the highest rating. The highest average IQRS rating for one journal was 3.04. The overall IQRS rating average across all four journals was 2.87, which is 2.13 rating scores lower than the ideal score of 5. There is room for improvement in the legends that accompany diagnostic images and figures in the oral and maxillofacial radiology literature. A proper legend provides an excellent diagnostic teaching opportunity for the reader and enhances the quality of a publication.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Background: To investigate the safety and efficacy of palliative arterial embolization for metastases of the scapula. Materials and Methods: We studied the files of the patients who underwent palliatrive arterial embolization for metastases of the scapula from January 2016 to January 2023, 15 patients (8 M, and 7 F) with a mean age of 62 years (SD 10.2) with two patients receiving a second procedure, for a total of 17 embolizations. Data on technical and clinical success, as well as changes in tumor size, were collected. All embolization-related complications were evaluated according to the CIRSE classification system for complications. Results: The average follow-up was 18 months (Range 6-36 months). The main primary tumor were renal, lung and breast cancer. The primary arteries embolized were the circumflex scapular artery, the dorsal scapular artery, and the thoracoacromial trunk. A mixture of Glubran and Lipiodol in a 1:2 ratio was used for embolization, with an average of 1.1 mL (Range 0.5-1.5 mL) used per procedure. The efficacy of embolization was measured using the Visual Analogue Scale (VAS), with values recorded at baseline, at 1, 3, 6, and 12 months post-embolization, and at 6-month intervals after. Baseline VAS scores averaged 7.2 (SD 1.5), decreasing to 2.8 (SD 0.9) at one month. Progressive reduction was observed at 3, 6, and 12 months with scores of 2.3 (SD 0.8), 1.9 (SD 0.7), and 1.6 (SD 0.6) respectively. Metastatic tumor size was reduced from a mean of 192.2 cm3 (range 38.7 to 480.1 cm3) pre-embolization to a mean of 177.3 cm3 (range 37.2 to 455.5 cm3) at the 12-month follow-up (p

Radiomics represents the science of extraction and analysis of a multitude of quantitative features from medical imaging, unrevealing quantitative potentiality of Radiologic images. This scientific review aims to provide radiologists a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and future prospects. Limits of standardization of actual scientific production are analyzed with the possible solutions proposed by some papers reported and commented. As the continuous evolution of medical images is ongoing, radiologists must be aware of new perspective to perform a central role in patients management.

The aim of this study was to explore the perceived impact of artificial intelligence (AI) on radiographers’ activities and profession in Switzerland. A survey conducted in the UK, translated into French and German, was disseminated through professional bodies and social media. The participants were Swiss radiographers (clinical/educators/ researchers/students) and physicians working within the medical imaging profession (radiology/nuclear medicine/radiation-oncology). The survey covered five sections: demographics, AI-knowledge, skills, confidence, perceptions about the AI impact. Descriptive, association statistics and qualitative thematic analysis were conducted. A total of 242 responses were collected (89% radiographers; 11% physicians). AI is being used by 43% of participants in clinical practice, but 63.8% of them did not feel confident with AI terminology. Participants viewed AI as an opportunity (57.2%), while 18.5% considered it as a threat. The opportunities were associated with streamlining repetitive tasks, minimizing errors, increasing time towards patient-centred care, research, and patient safety. The significant threats identified were reduction on work positions (22.6%), decrease of the radiographers’ expertise level due to automation bias (16.4%). Participants (68.3%) did not feel well trained/prepared to implement AI in their practice, highlighting the non-availability of specific training (87.6%). 93% of the participants mentioned that AI education should be included at undergraduate education program. Although most participants perceive AI as an opportunity, this study identified areas for improvement including lack of knowledge, educational supports/training, and confidence in radiographers. Customised training needs to be implemented to improve clinical practice and understanding of how AI can benefit radiographers.

: Background: A wide variety of coils are available for vascular embolization. This study aims to evaluate the safety and efficacy of a new Prestige coil. Method: Retrospective analysis of a multicentre registry data collected between February 2022 and November 2022. The choice of the embolization agent used was left at the investigator’s discretion to treat peripheral vascular anomalies. Patients for whom at least one Prestige coil was used were included in Series 1. All other patients were included in Series 2. Efficacy and safety were evaluated. Patients were followed up for one month. Results: 220 patients were included, 110 in each series. Patients were 149 men (67.7%) and 71 women (32.3%), of median age 62.5 years [IQR: 35.8-73], similar in the two series. The complete occlusion of the targeted vessel was reported in 96.4% (N=106/110) in Series 1 and in 99.7% (N=109/110) in Series 2. 4 patients experienced non-serious adverse events (1.8%, N=4/220): 1 back pain, 1 vomiting in Series 1, 1 off-target embolization and 1 puncture site hematoma in Series 2. 16 patients (7.2%, N=16/220) were lost to follow up. Improvement of the patient’s general state at one month was reported in 79.0% (N=83/105) in Series 1 and in 74.7% (N=74/99) in Series 2. 10 deaths occurred, 5 in Series 1 (4.8 %, N=5/105) and 5 in Series 2 (5.1%, N=5/99). They all concerned critically ill patients embolized for emergent arterial bleeding. Conclusion: the 1-month follow-up showed that the Prestige coils, alone or in combination, are efficient and safe.

Artificial intelligence is dramatically transforming medical imaging. We assessed the levels of artificial intelligence use among radiology trainees and explored their perceived impact of artifi-cial intelligence on the radiology workflow and radiology profession, in correlation with the perceived ease of use and behavioral intention to use artificial intelligence. This cross-sectional study enrolled radiology trainees from Saudi Arabia, and an online 5-part-structured question-naire was disseminated via online networks to trainee in July 2021. We included 98 participants (51 male; age 27.59±2.02 years). Level of use was low; few used it in routine practice (7%). The impact of artificial intelligence on the radiology workflow was positively perceived in all radi-ology workflow steps (3.64–3.97 out of 5). A positive impact on the radiology profession was more frequently perceived for technical and performance aspects (81%–85%) compared with prestige and legal aspects (64%–71%). Perceived ease of use and behavioral intention to use arti-ficial intelligence was associated with the current professional activity, level of use artificial in-telligence use, and perceived impact on the profession as well as on radiology workflow (p<0.05). Levels of artificial intelligence use in radiology are very low. The perceived positive impact of ar-tificial intelligence on radiology workflow and profession is correlated with an increase in be-havioral intention to use artificial intelligence. Thus, increasing awareness about the favorable impact can improve the behavioral use.

Artificial intelligence (AI) tools, such as OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have great potential in various fields, such as healthcare, education, and industry. This review highlights the applications and challenges of the ChatGPT versions 3.5 and 4.0 in radiology. ChatGPT can process medical texts, analyze medical images, and generate reports and summaries for different audiences. ChatGPT can also answer patients' questions and translate complex medical information into a plain language. ChatGPT has shown impressive results in medical tests and quizzes, indicating its potential to enhance diagnostic accuracy and medical education. These models show promise for reducing diagnosis times and healthcare costs. However, ChatGPT also faces challenges and limitations, such as a lack of visual content, ethical and legal issues, data quality, and error detection. This review highlights the benefits and future directions of ChatGPT and artificial intelligence (AI) in radiology.

Non-targeted effects (NTE) such as bystander effects or genomic instability have been known for many years but their significance for radiotherapy or medical diagnostic radiology are far from clear. Central to the issue are reported differences in response of normal and tumour tissues to signals from directly irradiated cells. This review will discuss possible mechanisms and implications of these different responses and will then discuss possible new therapeutic avenues suggested by the analysis. Finally, the importance of NTE for diagnostic radiology and nuclear medicine which stems from the dominance of NTE in the low dose region of the dose response curve will be presented. Areas such as second cancer induction and microenvironment plasticity will be discussed.

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides ground for technology development for computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive and clinically-oriented overview of DL-based segmentation studies for bladder, prostate, cervical and rectal cancers, highlighting the key findings, challenges and limitations.

Food handlers’ medical examinations are mandated by most countries as a way of safeguarding the health and safety of consumers. Food-borne diseases are an important cause of morbidity and mortality. In Zimbabwe, the use of chest radiographs and throat and rectal swab tests are a requirement during food handlers’ medical examinations. This study aimed at exploring the patterns and outcomes of physical medical examinations, chest radiographs, and other tests of food handlers. A cross-sectional review of retrospective occupational health records was carried out. The mean age for the study population was 37 years with an age range of 21 to 56 years. Males accounted for 73% of the study participants. All of the 157 rectal swabs were normal and did not culture any organism. Fifteen percent (24) of the throat swabs cultured one or more organisms. Ninety-seven percent of chest radiographs were normal. Ninety-seven percent of employees were certified as fit. Thirty-six percent of the food handlers were in the overweight and obese categories. Hypertension and high blood pressure were common conditions in the study sample. It can be concluded that routine radiological and laboratory testing of the food handlers in this study was of little value.

This multi-center retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8% – 97.5%] and a specificity of 97.3% [95% CI: 96.2% - 98.1%]. The application classified positive cases by AD types with an accuracy of 99.5% [95% CI: 98.9% – 99.8%] for type A and 97.5 [95% CI: 96.4% – 98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Endovascular therapy (EVT) has revolutionized the management of acute ischaemic strokes with large vessel occlusion, with emerging evidence suggesting its benefit also in large infarct core volume strokes. In the last 2 years, 4 randomised controlled trials have been published on this topic - RES-CUE-Japan-LIMIT, ANGEL-ASPECT, SELECT2 and TENSION, with overall results showing that EVT improves functional and neurological outcomes compared to medical management alone. This review seeks to summarise the recent evidence presented by these 4 trials and highlight some of the limitations of our current understanding on this topic.

Chest trauma is a critical medical condition that encompasses a range of injuries and can result from various causes. Prompt and accurate diagnosis is crucial in managing chest trauma patients and radiology plays a pivotal role in this aspect. Imaging modalities in chest trauma aid in precise diagnosis and treatment planning. Especially, X-rays remain the initial imaging modality of choice due to their accessibility, speed, and ability to detect acute life-threatening conditions. Computed Tomography (CT) Scan offers detailed cross-sectional images allowing for comprehensive evaluation of chest trauma and surgical planning when necessary. Ultrasound (US) is increasingly valuable tool due to its portability and real-time imaging capabilities, aiding in rapid assessment at the bedside. Magnetic Resonance Imaging (MRI) is less commonly used due to time constraints and limited availability. Radiological imaging serves various purposes in the diagnosis and management of chest trauma such as early detection and triage, characterization and severity assessment, surgical planning, follow-up and monitoring of chest trauma. Reduction of radiation exposure, advancements in imaging technology (dual-energy CT, contrast-enhanced imaging) and Artificial Intelligence (AI) algorithms are crucial challenges and deserve attention. A multidisciplinary approach integrating radiological expertise remains fundamental in ensuring optimal outcomes for patients with chest trauma.

The advent of AI in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. With the growing number of AI applications and commercially available tools, there is an increase in their usage in orthodontic practices worldwide. This review aims to explore the principles of artificial intelligence (AI), its applications in the diagnostic process of modern orthodontic practices, and concerns associated with the implementation of AI algorithms in clinical practice. A comprehensive review of the literature was conducted, focusing on five categories where AI has been applied in orthodontics: dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, and extraction decision making. AI has demonstrated high efficacy in all those fields. However, variations in performance and the necessity of manual supervision indicate that AI should be used cautiously in clinical settings. Nevertheless, the high complexity and potential unpredictability of AI algorithms call for cautious implementation and regular manual validation of results. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for the successful integration of AI into orthodontic practice.

.Background: Neither radiological phenotypic characteristics nor reconstruction CT scan to study the cranial contour in children with the so called the idiopathic type of West syndrome has been implemented to reach the etiology understanding. Material and Methods: The basic diagnostic measures and the traditional antiepileptic treatments have been applied to these children in accordance with the conventional protocol of investigations and treatment for children with West syndrome. Boys from three unrelated families were given the diagnosis of idiopathic type of West syndrome. Three boys aged 6,7,9 and three parents (age range of 28-41 year) were included in this study. These children and their parents were referred to our orthopedic departments because of variable skeletal deformities. All showed the history of intellectual disabilities, cryptogenic epileptic spasms and fragmented hypsarrhythmia. Recently, variable forms skeletal deformities of flat foot, torticollis and early onset osteoarthritis. We performed detailed clinical and radiological phenotypic characterization of every affected child, siblings and parents. All affected children underwent whole exome sequence analysis Results: The craniofacial phenotype of all children revealed apparent disruption of development of the cranial bones. Palpation of the skull bones showed unusual palpable boney ridges along different sutural locations. A-7-year-old child showed abnormal bulging over the sagittal suture associated with bilateral boney ridges over the sequamosal sutures. Examining his parents, we noticed a similar skull radiographic abnormality in his mother radiographs. A-10-year-old boy showed an extremely narrow frontal area, facial asymmetry and a well palpable ridge over the lambdoid sutures. 3D reconstruction CT scan confirmed the diagnosis of early closure . His mother showed typical radiographic skull phenotype with a history of postadulthood scoliosis. A- 12-year-old boy showed brachycephaly. Investigating his parents revealed a brachycephalic mother with border line intellegence. We affirm that the pattern of inheritance in the three boys was compatible with X-linked recessive pattern of inheritance . Whole exome sequencing showed non definite phenotype/genotype correlation. Conclusion: The aim of this study was fivefold. First, to refute the common usage of the term idiopathic and we affirm that our findings are Novel. Second, it could be possible that West syndrome is a symptom complex rather than a separate diagnostic entity. Third, to further detect the genetic carrier , we explored the connection between the cranial bones in the children with West syndrome with what is seen in parents.. Fourth, the differences in cranial bones abnormalities among every child and another seems to be heterogeneous. Fifth, it seems that the progressive deceleration in the development of this group of children is highly connected to the progressive closure of the cranial sutures.

The advent of Large Language Models like Generative Pre-trained Transformer 4 (GPT-4) has introduced a revolutionary approach to bridging the communication gap between complex radiology reports and patient comprehension. This review article explores the pivotal role of GPT-4 in generating patient-friendly summaries of radiology reports, aiming to demystify medical jargon and enhance patient engagement and understanding. Through an examination of the technological underpinnings of GPT-4, this article highlights its capability to accurately interpret and simplify radiological findings into layman's terms. It assesses the impact of such advancements on patient satisfaction, adherence to treatment plans, and overall healthcare outcomes. Furthermore, the review delves into the potential challenges and ethical considerations associated with implementing AI-driven summaries in clinical practice, including accuracy, privacy, and the need for human oversight. By offering insights into the current applications, benefits, and limitations of GPT-4 in radiology, this short review underscores the transformative potential of AI in fostering a more inclusive and patient-centric healthcare communication paradigm.

COVID-19 has brought unprecedented challenges and exacerbated existing work-related struggles resulting in heightened occupational stress and impacted work performance among health caregivers. This study utilised the transactional model of stress and coping to explore the coping strategies adopted by radiology health caregivers during the pandemic with the aim to provide baseline data for enhancing preparedness and development of interventions to support staff during future health crises. A qualitative, interpretative phenomenological analysis (IPA) methodology coupled with constructivist and interpretivist philosophical views were utilised in a multi-method context to conduct semi-structured interviews and focus group discussions among 24 radiology health caregivers in the eThekwini district of KwaZulu-Natal, South Africa. Data were transcribed verbatim and manually analysed using the IPA framework. The radiology health caregivers highlighted resilience, adaptability and identified emotion-focused and problem-focused coping as their mostly preferred strategies of dealing with COVID-19 related mental distress. These included accepting professional responsibilities and duties, social coping, spiritual therapy, adaptive behavioural coping, escape-avoidance and solution-oriented approaches. The findings can guide radiology departments in developing bespoke mental health interventions to support radiology health caregivers during any future health crises.

In the literature, the question of the amount of data necessary and sufficient to validate different models of the occurrence of risk of adverse events for patients or the classification of the presence or absence of pathological features has been repeatedly raised. In the presented study, we propose a new approach to determine the necessary and enough studies for validation of medical software based on artificial intelligence technology, whose main task is to classify medical X-rays according to the presence of normality and pathology. It is shown that for several studies in a dataset, when AUC ROC has maximum heterogeneity, it varies depending on the balance of "norm"/"pathology" classes. Thus, for a balance of "normal"/"abnormality", where 90% is "normal" and 10% is "abnormality", maximum heterogeneity is achieved for 190 studies, for a balance of 80% ("normal")/20% ("abnormality") for 80 studies, for a balance of 70% ("norm")/30% ("abnormality") - 120 trials, for a balance of 60% ("norm")/40% ("abnormality") the maximum heterogeneity is reached at 110 trials, and for a balance of 50% ("norm")/50% ("abnormality") - at 70 trials. The obtained data are in good agreement with the previous results. They allow us to determine a sufficient (necessary) number of studies in the dataset to perform an unbiased assessment of AUC ROC.

Transarterial embolization (TAE) for high-flow pancreaticoduodenal artery (PDA) aneurysms in patients with celiac-trunk stenosis by the median arcuate ligament (MAL) has been found effective both after rupturing and to prevent rupture. The objective was to describe the TAE techniques used and their effectiveness in excluding PDA aneurysms due to MAL syndrome. This single-center retrospective study done at the Dijon-Bourgogne University Hospital included all patients treated by TAE in 2010–2022 for ruptured or unruptured high-flow PDA aneurysms caused by MAL syndrome. We identified 14 patients (7 women and 7 men; mean age, 64 years). Packing and trapping techniques were used alone or together. Occlusion was with microcoils, co-polymer, or cyanoacrylate glue, used separately or combined. Technical success was achieved in 13 (93%) patients. Clinical success was achieved in 12 (86%) patients. One major and two minor complications were recorded within the first 30 days. No complications occurred after 30 days. Follow-up ranged from 1 to 84 months. No cases of aneurysm recanalization were recorded to date. TAE had high technical and clinical success rates in our patients with unruptured or ruptured PDA aneurysms due to MAL syndrome.

This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making its foray into radiology, a move that is catalysing transformational shifts in the healthcare landscape. It sheds light on the journey of radiology, from the pioneering discovery of X-rays to today’s intricate imaging technologies, infused with machine learning and deep learning in medical image analysis. At the crux of this review lies an in-depth study of AI applications in radiology, elucidating its seminal roles in image segmentation, computer-aided diagnosis, predictive analytics, and workflow optimisation. A spotlight is cast on the profound impact of AI on diagnostic processes, personalised medicine, and clinical workflows, with empirical evidence derived from a series of case studies across multiple medical disciplines. However, the integration of AI in radiology is not devoid of challenges. The review ventures into the labyrinth of obstacles that are inherent to AI-driven radiology — data quality, the ’black box’ enigma, infrastructural and technical complexities, as well as ethical implications. Peering into the future, the review contends that the road ahead for AI in radiology is paved with promising opportunities. It advocates for continuous research, embracing avant-garde imaging technologies, and fostering robust collaborations between radiologists and AI developers. It concludes by firmly cementing the role of AI as a catalyst for change in radiology, a stance that is firmly rooted in sustained innovation, dynamic partnerships, and a steadfast commitment to ethical responsibility.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of Cone Beam Computed Tomography with Magnetic Resonance Imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.

Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA approved AI applications for breast imaging, but adoption and utilization are widely variable and overall low. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer and many of the most exciting research and available AI applications focus on cancer detection for mammography. There are however multiple additional potential applications for AI in breast imaging including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.