Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection
Rohit Kunnath Menon
TL;DR
This study tested an AI tool for detecting dental issues in radiographs and found it to be highly accurate but with some specific areas needing improvement.
Contribution
The study evaluates a prototype AI system's diagnostic accuracy for dental pathologies using radiographs and identifies specific limitations.
Findings
The AI prototype showed high sensitivity and specificity for most dental pathologies.
Key errors included misidentifying fillings as crowns and missing impacted canines and secondary caries.
Periapical radiograph analysis had all test characteristics above 85%.
Abstract
Background/Objectives: It is essential to test the accuracy of artificial intelligence-assisted tools that detect dental pathologies from radiographs. This study aimed to evaluate the test characteristics of an artificial intelligence-assisted convolutional neural network-based prototype used for automated radiographic detection. Methods: A total of 300 panoramic and 100 intraoral periapical radiographs were collected between January 2020 and 2024 and then analyzed by two trained, independent specialist evaluators. The diagnostic consensus, “ground truth”, was labeled as follows: BL: bone loss; C: caries; F: filling; I: implants; IT: impacted teeth; P: prosthesis; PC: post-core; PR: periapical radiolucency; RF: root fillings; and RR: retained roots. The radiographs were uploaded to the prototype, and the results were compared. Sensitivity, specificity, positive predictive value, and…
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Taxonomy
TopicsDental Radiography and Imaging · Artificial Intelligence in Healthcare and Education · Dental Research and COVID-19