Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement

Assessing bone conditions around teeth through radiographs is essential for accurate periodontal diagnosis. The effectiveness of radiographic interpretation relies heavily on the clinician's experience and expertise. It will be beneficial to have an artificial intelligence-driven platform that can provide standardized and reliable radiographic bone assessment results. This study used 6,552 dental radiographs to develop multiple deep learning models. A platform integrated with these deep learning models was created to assess bone conditions around teeth and suggest a case-level periodontal diagnosis based on dental radiographs. A validated survey with a score ranging from 0 to 100 was used to collect care providers' feedback after testing the platform. Our results showed that the platform demonstrated high accuracy in all diagnostic categories, including extent, stage, and grade, of clinical cases. The mean survey scores for 20 dental students, 20 dental residents, and 20 dentists were 79.13 ± 12.01, 74.13 ± 16.17, and 80.75 ± 15.24, respectively. Overall, the deep learning-based diagnostic platform can reliably assess radiographic bone conditions and generate reliable case-level diagnoses. The usability of this platform is well accepted by healthcare providers. As an adjunct diagnostic tool, this platform can assist clinicians in making accurate periodontal diagnoses, leading to more effective treatment planning.