(1) Teeth, represent in humans the most resilient tissues. However, exposure to concentrated acids might lead to their obliteration, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper introduces novel method of 3D reconstruction of dental patterns as a crucial step for digital identification with dental records.; (2) With combination of modern methods of Micro-Computer Tomography, Cone Beam Computer Tomography, Attenuated Total Reflection in conjunction with Fourier-Transform Infrared Spectroscopy and Artificial Intelligence Convolutional Neural Network algorithms, the paper presents the way of 3D dental pattern reconstruction and human remains identification. Research studies morphology of teeth, bone, and dental materials (Amalgam, Composite, Glass-ionomer cement) under different periods of exposure to 75% sulfuric acid; (3) Results reveal significant volume loss in bone, enamel, dentine, and as well glass-ionomer cement. Results also reveal significant resistance of composite and amalgam dental materials to sulfuric acid impact, thus serving as strong parts in the dental pattern mosaic. Paper also introduces probably first successful artificial intelligence application in automated forensic CBCT segmentation.; (4) Interdisciplinary cooperation utilizing mentioned technologies can solve problem of human remains identification with 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

Omicron variant is more invasive against immunity, but it's not more invasive in any change in degree against masks, and indoor air; than previous variants. With its arrival, dentistry is facing another challenge. With a mission to protect both patients and healthcare workers, we are adapting to the current epidemiologic situation and anticipate the incoming change. This article presents an unpublished prospective setting for dental care in the new chapter of pandemics after Omicron variant. Introduced biosafety protocol has been clinically tested for 18 months in the field. Three fundamental pillars of this sustainable biosafety protocol are: (1) UVC air disinfection; (2) air saturation with certified virucidal oils through nebulizing diffusers; (3) telehealth solutions. As a method of evaluation pseudonymous on-line smart form was used. This biosafety protocol is not relying on persons` identification as uninfected; it is designed for environments where healthcare workers or patients are hypothetical asymptomatic carriers. Results from 115 patient feedbacks suggest that with this protocol in place, there was no observed or reported translation of infection from patient to another patient or from patient to doctor or nurse and vice versa, albeit nine of the patients have retrospectively admitted being probably infectious during their dental appointment. The key of this protocol is its clinical sustainability, as full-body protective suits don’t represent sustainable dental care as well as there is no acceptable risk of patients getting infected during dental procedures.