The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain
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...
| Үндсэн зохиолчид: | , , , , , , |
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MDPI AG
2023-09-01
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| Цуврал: | Diagnostics |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://www.mdpi.com/2075-4418/13/17/2840 |
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| author | Dittapong Songsaeng Poonsuta Nava-apisak Jittsupa Wongsripuemtet Siripra Kingchan Phuriwat Angkoondittaphong Phattaranan Phawaphutanon Akara Supratak |
| author_facet | Dittapong Songsaeng Poonsuta Nava-apisak Jittsupa Wongsripuemtet Siripra Kingchan Phuriwat Angkoondittaphong Phattaranan Phawaphutanon Akara Supratak |
| author_sort | Dittapong Songsaeng |
| collection | DOAJ |
| description | 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 an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. 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 the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists’ diagnostic prowess in identifying hydrocephalus. |
| first_indexed | 2024-03-10T23:26:10Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T23:26:10Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c9514f86a7934fbcae582eb49e79ff872023-11-19T08:00:18ZengMDPI AGDiagnostics2075-44182023-09-011317284010.3390/diagnostics13172840The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the BrainDittapong Songsaeng0Poonsuta Nava-apisak1Jittsupa Wongsripuemtet2Siripra Kingchan3Phuriwat Angkoondittaphong4Phattaranan Phawaphutanon5Akara Supratak6Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, ThailandDepartment of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, ThailandDepartment of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, ThailandFaculty of Information and Communication Technology, Mahidol University, Salaya, Nakhon Pathom 73170, ThailandFaculty of Information and Communication Technology, Mahidol University, Salaya, Nakhon Pathom 73170, ThailandDepartment of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, ThailandFaculty of Information and Communication Technology, Mahidol University, Salaya, Nakhon Pathom 73170, ThailandDiagnosing 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 an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. 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 the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists’ diagnostic prowess in identifying hydrocephalus.https://www.mdpi.com/2075-4418/13/17/2840NPHradiologic markershydrocephalusAI |
| spellingShingle | Dittapong Songsaeng Poonsuta Nava-apisak Jittsupa Wongsripuemtet Siripra Kingchan Phuriwat Angkoondittaphong Phattaranan Phawaphutanon Akara Supratak The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain Diagnostics NPH radiologic markers hydrocephalus AI |
| title | The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain |
| title_full | The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain |
| title_fullStr | The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain |
| title_full_unstemmed | The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain |
| title_short | The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain |
| title_sort | diagnostic accuracy of artificial intelligence in radiological markers of normal pressure hydrocephalus nph on non contrast ct scans of the brain |
| topic | NPH radiologic markers hydrocephalus AI |
| url | https://www.mdpi.com/2075-4418/13/17/2840 |
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