Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Abstract

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 (±0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 (±0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 (±0.04), and an F1 score of 0.58 (±0.04) corresponding to a PPV of 0.67 (±0.05) and TPR of 0.51 (±0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

“…The current study of 34 published documents identified 8 articles [ 5 , 6 , 12 – 15 , 31 , 39 ] that made direct comparisons between the diagnostic accuracy of machine learning models and human clinicians. Of the 15 points evaluated from the MI-CLAIM checklist, all but one paper [ 39 ] scored over 13.…”

“…The majority of the periodontal pain was associated with periodontal bone loss and root attachment loss which were, therefore, the primary quantification parameters [ 5 ]. Clinicians' experience was assumed to play a critical role in dictating the overall accuracy of radiographic differential diagnosis in machine learning.…”

“…The pain may arise from exposed dentin (hypersensitivity pain) [ 2 ] or from carious infection of the dental pulp (pulpitis) [ 3 ]. Untreated dental pulp encourages the infection to spread through the root canals into the periodontal tissue (apical periodontitis) [ 4 , 5 ] and may cause swelling, infection, and bone loss (periapical abscess) [ 6 ]. Periodontal tissue can also be painfully infected without carious activity (gingivitis and periodontitis) [ 7 ].…”

“…The clinician's ability to diagnose such events swiftly and accurately is pivotal in successful patient management. However, various studies have shown that incorrect diagnoses are fairly common among clinicians in such situations [ 5 , 6 , 18 ]. While pain itself might not be reliably quantified, machine learning/artificial intelligence (AI) has been recently deployed to detect and quantify various diseases which elicit pain within the orofacial region to aid in accurate diagnostics and management.…”

German Dentists’ Preferences for the Treatment of Apical Periodontitis: A Cross-Sectional Survey

“…This could be fulfilled by reviewing and re-evaluating the endodontic education programs on under- and postgraduate levels, as well as targeting the diagnostic and clinical skills of the dentists and also improving their self-efficacy and self-perceived competence [ 53 ]. Equally important, advanced diagnostic methods, such as artificial intelligence-guided techniques, should be introduced to support the clinical decision-making process based on scientific evidence and train the dentists to correctly interpret and diagnose the conditions [ 54 ]. In addition, further evaluation and consideration of the factors affecting the treatment decisions of AP (ERF, PRF, and TRF) should be performed to establish scientifically-based treatment guidelines for AP and guarantee a dental health system allowing the correct treatment of the patients.…”