Falk Schwendicke1, Tatiana Golla2, Martin Dreher2, Joachim Krois2. 1. Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany. Electronic address: falk.schwendicke@charite.de. 2. Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany.
Abstract
OBJECTIVES: Convolutional neural networks (CNNs) are increasingly applied for medical image diagnostics. We performed a scoping review, exploring (1) use cases, (2) methodologies and (3) findings of studies applying CNN on dental image material. SOURCES: Medline via PubMed, IEEE Xplore, arXiv were searched. STUDY SELECTION: Full-text articles and conference-proceedings reporting CNN application on dental imagery were included. DATA: Thirty-six studies, published 2015-2019, were included, mainly from four countries (South Korea, United States, Japan, China). Studies focussed on general dentistry (n = 15 studies), cariology (n = 5), endodontics (n = 2), periodontology (n = 3), orthodontics (n = 3), dental radiology (2), forensic dentistry (n = 2) and general medicine (n = 4). Most often, the detection, segmentation or classification of anatomical structures, including teeth (n = 9), jaw bone (n = 2) and skeletal landmarks (n = 4) was performed. Detection of pathologies focused on caries (n = 3). The most commonly used image type were panoramic radiographs (n = 11), followed by periapical radiographs (n = 8), Cone-Beam CT or conventional CT (n = 6). Dataset sizes varied between 10-5,166 images (mean 1,053). Most studies used medical professionals to label the images and constitute the reference test. A large range of outcome metrics was employed, hampering comparisons across studies. A comparison of the CNN performance against an independent test group of dentists was provided by seven studies; most studies found the CNN to perform similar to dentists. Applicability or impact on treatment decision was not assessed at all. CONCLUSIONS: CNNs are increasingly employed for dental image diagnostics in research settings. Their usefulness, safety and generalizability should be demonstrated using more rigorous, replicable and comparable methodology. CLINICAL SIGNIFICANCE: CNNs may be used in diagnostic-assistance systems, thereby assisting dentists in a more comprehensive, systematic and faster evaluation and documentation of dental images. CNNs may become applicable in routine care; however, prior to that, the dental community should appraise them against the rules of evidence-based practice.
OBJECTIVES: Convolutional neural networks (CNNs) are increasingly applied for medical image diagnostics. We performed a scoping review, exploring (1) use cases, (2) methodologies and (3) findings of studies applying CNN on dental image material. SOURCES: Medline via PubMed, IEEE Xplore, arXiv were searched. STUDY SELECTION: Full-text articles and conference-proceedings reporting CNN application on dental imagery were included. DATA: Thirty-six studies, published 2015-2019, were included, mainly from four countries (South Korea, United States, Japan, China). Studies focussed on general dentistry (n = 15 studies), cariology (n = 5), endodontics (n = 2), periodontology (n = 3), orthodontics (n = 3), dental radiology (2), forensic dentistry (n = 2) and general medicine (n = 4). Most often, the detection, segmentation or classification of anatomical structures, including teeth (n = 9), jaw bone (n = 2) and skeletal landmarks (n = 4) was performed. Detection of pathologies focused on caries (n = 3). The most commonly used image type were panoramic radiographs (n = 11), followed by periapical radiographs (n = 8), Cone-Beam CT or conventional CT (n = 6). Dataset sizes varied between 10-5,166 images (mean 1,053). Most studies used medical professionals to label the images and constitute the reference test. A large range of outcome metrics was employed, hampering comparisons across studies. A comparison of the CNN performance against an independent test group of dentists was provided by seven studies; most studies found the CNN to perform similar to dentists. Applicability or impact on treatment decision was not assessed at all. CONCLUSIONS: CNNs are increasingly employed for dental image diagnostics in research settings. Their usefulness, safety and generalizability should be demonstrated using more rigorous, replicable and comparable methodology. CLINICAL SIGNIFICANCE: CNNs may be used in diagnostic-assistance systems, thereby assisting dentists in a more comprehensive, systematic and faster evaluation and documentation of dental images. CNNs may become applicable in routine care; however, prior to that, the dental community should appraise them against the rules of evidence-based practice.