Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o
<b>Background/Objectives:</b> The role of artificial intelligence (AI) in radiological image analysis is rapidly evolving. This study evaluates the diagnostic performance of Chat Generative Pre-trained Transformer Omni (GPT-4 Omni) in detecting intracranial hemorrhages (ICHs) in non-cont...
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MDPI AG
2025-01-01
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| author | Mustafa Koyun Zeycan Kubra Cevval Bahadir Reis Bunyamin Ece |
| author_facet | Mustafa Koyun Zeycan Kubra Cevval Bahadir Reis Bunyamin Ece |
| author_sort | Mustafa Koyun |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> The role of artificial intelligence (AI) in radiological image analysis is rapidly evolving. This study evaluates the diagnostic performance of Chat Generative Pre-trained Transformer Omni (GPT-4 Omni) in detecting intracranial hemorrhages (ICHs) in non-contrast computed tomography (NCCT) images, along with its ability to classify hemorrhage type, stage, anatomical location, and associated findings. <b>Methods:</b> A retrospective study was conducted using 240 cases, comprising 120 ICH cases and 120 controls with normal findings. Five consecutive NCCT slices per case were selected by radiologists and analyzed by ChatGPT-4o using a standardized prompt with nine questions. Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by comparing the model’s results with radiologists’ assessments (the gold standard). After a two-week interval, the same dataset was re-evaluated to assess intra-observer reliability and consistency. <b>Results:</b> ChatGPT-4o achieved 100% accuracy in identifying imaging modality type. For ICH detection, the model demonstrated a diagnostic accuracy of 68.3%, sensitivity of 79.2%, specificity of 57.5%, PPV of 65.1%, and NPV of 73.4%. It correctly classified 34.0% of hemorrhage types and 7.3% of localizations. All ICH-positive cases were identified as acute phase (100%). In the second evaluation, diagnostic accuracy improved to 73.3%, with a sensitivity of 86.7% and a specificity of 60%. The Cohen’s Kappa coefficient for intra-observer agreement in ICH detection indicated moderate agreement (κ = 0.469). <b>Conclusions:</b> ChatGPT-4o shows promise in identifying imaging modalities and ICH presence but demonstrates limitations in localization and hemorrhage type classification. These findings highlight its potential for improvement through targeted training for medical applications. |
| format | Article |
| id | doaj-art-1fecd04fa0ea42f4ad099020a6996e5d |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-1fecd04fa0ea42f4ad099020a6996e5d2025-01-24T13:28:53ZengMDPI AGDiagnostics2075-44182025-01-0115214310.3390/diagnostics15020143Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4oMustafa Koyun0Zeycan Kubra Cevval1Bahadir Reis2Bunyamin Ece3Department of Radiology, Kastamonu Training and Research Hospital, Kastamonu 37150, TurkeyDepartment of Radiology, Kastamonu Training and Research Hospital, Kastamonu 37150, TurkeyDepartment of Radiology, Kastamonu University, Kastamonu 37150, TurkeyDepartment of Radiology, Kastamonu University, Kastamonu 37150, Turkey<b>Background/Objectives:</b> The role of artificial intelligence (AI) in radiological image analysis is rapidly evolving. This study evaluates the diagnostic performance of Chat Generative Pre-trained Transformer Omni (GPT-4 Omni) in detecting intracranial hemorrhages (ICHs) in non-contrast computed tomography (NCCT) images, along with its ability to classify hemorrhage type, stage, anatomical location, and associated findings. <b>Methods:</b> A retrospective study was conducted using 240 cases, comprising 120 ICH cases and 120 controls with normal findings. Five consecutive NCCT slices per case were selected by radiologists and analyzed by ChatGPT-4o using a standardized prompt with nine questions. Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated by comparing the model’s results with radiologists’ assessments (the gold standard). After a two-week interval, the same dataset was re-evaluated to assess intra-observer reliability and consistency. <b>Results:</b> ChatGPT-4o achieved 100% accuracy in identifying imaging modality type. For ICH detection, the model demonstrated a diagnostic accuracy of 68.3%, sensitivity of 79.2%, specificity of 57.5%, PPV of 65.1%, and NPV of 73.4%. It correctly classified 34.0% of hemorrhage types and 7.3% of localizations. All ICH-positive cases were identified as acute phase (100%). In the second evaluation, diagnostic accuracy improved to 73.3%, with a sensitivity of 86.7% and a specificity of 60%. The Cohen’s Kappa coefficient for intra-observer agreement in ICH detection indicated moderate agreement (κ = 0.469). <b>Conclusions:</b> ChatGPT-4o shows promise in identifying imaging modalities and ICH presence but demonstrates limitations in localization and hemorrhage type classification. These findings highlight its potential for improvement through targeted training for medical applications.https://www.mdpi.com/2075-4418/15/2/143ChatGPTartificial intelligenceintracranial hemorrhagecomputed tomographyradiology |
| spellingShingle | Mustafa Koyun Zeycan Kubra Cevval Bahadir Reis Bunyamin Ece Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o Diagnostics ChatGPT artificial intelligence intracranial hemorrhage computed tomography radiology |
| title | Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o |
| title_full | Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o |
| title_fullStr | Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o |
| title_full_unstemmed | Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o |
| title_short | Detection of Intracranial Hemorrhage from Computed Tomography Images: Diagnostic Role and Efficacy of ChatGPT-4o |
| title_sort | detection of intracranial hemorrhage from computed tomography images diagnostic role and efficacy of chatgpt 4o |
| topic | ChatGPT artificial intelligence intracranial hemorrhage computed tomography radiology |
| url | https://www.mdpi.com/2075-4418/15/2/143 |
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