Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal Pressure Hydrocephalus (NPH) on Non- Contrast CT Scan of the Brain

Dittapong Songsaeng
,
Akara Supratak
* ORCID logo ,
Poonsuta Nava-apisak
,
Jittsupa Wongsripuemtet
ORCID logo ,
Siripra Kingchan
,
Phuriwat Angkoondittaphong
,
Phattaranan Phawaphutanon
ORCID logo
Version 1 : Received: 22 June 2023 / Approved: 22 June 2023 / Online: 22 June 2023 (12:44:03 CEST)

A peer-reviewed article of this Preprint also exists.

Songsaeng, D.; Nava-apisak, P.; Wongsripuemtet, J.; Kingchan, S.; Angkoondittaphong, P.; Phawaphutanon, P.; Supratak, A. The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain. Diagnostics 2023, 13, 2840. Songsaeng, D.; Nava-apisak, P.; Wongsripuemtet, J.; Kingchan, S.; Angkoondittaphong, P.; Phawaphutanon, P.; Supratak, A. The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain. Diagnostics 2023, 13, 2840.

Abstract

Diagnosing Normal Pressure Hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans' index, narrow sulci at high parietal convexity, Sylvian fissures' dilation, focally enlarged sulci, and more, are currently measured by radiologists. This study aimed to enhance NPH diagnosis by comparing the accuracy, sensitivity, specificity, and predictive values of radiological parameters, as evaluated by radiologists and AI methods utilizing cerebrospinal fluid volumetry. Results revealed a sensitivity of 77.14% for radiologists and 99.05% for AI, with specificities of 98.21% and 57.14%, respectively, in diagnosing NPH. Radiologists demonstrated NPV, PPV, and accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%. ROC curves exhibited an area under the curve of 0.954 for radiologists and 0.784 for AI, signifying the diagnostic index for NPH. In conclusion, although radiologists exhibited superior sensitivity, specificity, and accuracy in diagnosing NPH, AI served as an effective initial screening mechanism for potential NPH cases, potentially easing the radiologists' burden. Given ongoing AI advancements, it's plausible that AI could eventually match or exceed radiologists' diagnostic prowess in identifying hydrocephalus.

Keywords

NPH; radiologic markers; hydrocephalus; AI

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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