Diagnostics (Basel, Switzerland) · 2023
The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach.
СтатьяНазвание и аннотация: Перевод в очереди
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Lee Kyu-Hong, Lee Ro-Woon, Lee Kyung-Hee, Park Won, Kwon Seong-Ryul, Lim Mie-Jin
Аннотация
PURPOSE: Sacroiliitis refers to the inflammatory condition of the sacroiliac joints, frequently causing lower back pain. It is often associated with systemic conditions. However, its signs on radiographic images can be subtle, which may result in it being overlooked or underdiagnosed. This study aims to utilize artificial intelligence (AI) to create a diagnostic tool for more accurate sacroiliitis detection in radiological images, with the goal of optimizing treatment plans and improving patient outcomes.
MATERIALS AND METHOD: The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1-4). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform.
RESULT: The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3).
CONCLUSIONS: The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.
MATERIALS AND METHOD: The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1-4). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform.
RESULT: The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3).
CONCLUSIONS: The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.