Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study
Issued Date
2025-01-01
Resource Type
eISSN
2561326X
DOI
Scopus ID
2-s2.0-105024357535
Journal Title
Jmir Formative Research
Volume
9
Rights Holder(s)
SCOPUS
Bibliographic Citation
Jmir Formative Research Vol.9 (2025)
Suggested Citation
Angkurawaranon S., Jitmahawong N., Unsrisong K., Thabarsa P., Madla C., Vuthiwong W., Sudsang T., Angkurawaranon C., Traisathit P., Inkeaw P. Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study. Jmir Formative Research Vol.9 (2025). doi:10.2196/81038 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113554
Title
Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study
Author's Affiliation
Corresponding Author(s)
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Abstract
Background: Early identification of the etiology of spontaneous intracerebral hemorrhage (ICH) could significantly contribute to planning a suitable treatment strategy. A notable radiomics-based artificial intelligence (AI) model for classifying causes of spontaneous ICH from brain computed tomography scans has been previously proposed. Objective: This study aimed to externally validate and assess the utility of this AI model. Methods: This study used 69 computed tomography scans from a separate cohort to evaluate the AI model’s performance in classifying nontraumatic ICHs into primary, tumorous, and vascular malformation related. We also assessed the accuracy, sensitivity, specificity, and positive predictive value of clinicians, radiologists, and trainees in identifying the ICH causes before and after using the model’s assistance. The performances were statistically analyzed by specialty and expertise levels. Results: The AI model achieved an overall accuracy of 0.65 in classifying the 3 causes of ICH. The model’s assistance improved overall diagnostic performance, narrowing the gap between nonradiology and radiology groups, as well as between trainees and experts. The accuracy increased from 0.68 to 0.72, from 0.72 to 0.76, from 0.69 to 0.74, and from 0.72 to 0.75 for nonradiologists, radiologists, trainees, and specialists, respectively. With the model’s support, radiology professionals demonstrated the highest accuracy, highlighting the model’s potential to enhance diagnostic consistency across different levels. Conclusions: When applied to an external dataset, the accuracy of the AI model in categorizing spontaneous ICHs based on radiomics decreased. However, using the model as an assistant substantially improved the performance of all reader groups, including trainees and radiology and nonradiology specialists.