Development of a web application for stroke diagnosis assistance using deep learning artificial intelligence on computed tomography image - Kaewlek, Titipong; Sitinwan , Ketmanee; Lueangaroon , Kunaporn; Sansuriyawong, Wasita; Pattaweerakul , Rawiwan; Hantrakul , Thatchai; Chinwatanawongwan , Bhuwid (The Thai Journal of Radiological Technology, 2024)
Introduction: Stroke causes a high risk among the elderly. Accurate diagnosis using computed tomography (CT) imaging is essential, and expert interpretation is required. Artificial intelligence (AI) has increasingly contributed to improving the accuracy of radiologists’ interpretations. Objective: This study aimed to develop a web application for stroke detection in brain CT images using artificial intelligence. Methods: A total of 1,636 brain CT images (1,111 normal images and 525 stroke images) were used in this study. Approximately 70% (1,175 images) of the dataset were used for model training, 20% (329 images) for validation, and 10% (132 images) for testing the deep learning model (VGG-16). Accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC) were used to evaluate the performance of the web application. Design and usability were evaluated using a 5-point Likert scale by three radiologists. Results: The results shows that the accuracy, sensitivity, specificity, F1-score, and AUC were 0.969, 0.952, 0.978, 0.952, and 0.965, respectively. The design and usability scores evaluating by radiologists were 4.13 ± 0.38 and 4.37 ± 0.33, respectively. Conclusion: The developed web application demonstrated high performance in diagnosing stroke from brain CT images and is easy to use via internet-based platforms.