ArticleMayo Clinic proceedings. Digital health2026
Diagnostic Accuracy of Clinical Decision Support Systems ORADIII and ORAD DDx to Histopathological Diagnosis of Jaw Lesions.
Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
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Authors and funding
8 authors.
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Abstract
Objective: To evaluate and compare the diagnostic performance of 2 clinical decision support system tools-ORADIII and ORAD DDx-against histopathological diagnosis in identifying intrabony jaw lesions using orthopantomograms. Patients and Methods: A diagnostic accuracy, cross-sectional study was conducted in the Department of Oral Medicine and Radiology, Kathmandu University School of Medical Sciences, Dhulikhel Hospital, Kavre, Nepal, from January 1, 2025, to April 30, 2025, after institutional review committee approval. The study was conducted on a sample comprising both lesion and nonlesion cases based on radiographic evaluation. Diagnostic outputs from ORADIII and ORAD DDx were compared with histopathology. Key performance indicators-including sensitivity, specificity, accuracy, F1 score, positive predictive value, negative predictive value, and likelihood ratios (positive and negative)-were calculated for both systems. Results: Among the 350 samples evaluated, including 175 lesion positive and 175 nonlesion cases, ORAD DDx demonstrated superior diagnostic performance compared with ORADIII. The sensitivity, specificity, accuracy, and F1 score for ORADIII were 64.57%, 60.00%, 62.28%, and 0.6314, respectively. In contrast, ORAD DDx achieved sensitivity, specificity, accuracy, and F1 score of 70.29%, 65.71%, 68.00%, and 0.687, respectively. Conclusion: ORAD DDx showed better diagnostic performance than ORADIII across most metrics, indicating its potential as a more reliable clinical decision support system for diagnosis decision support for intrabony jaw lesions. This could also be due to its categorizing of lesions and variations. Further validation with larger, stratified, and multicenter data sets is recommended.
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