Please use this identifier to cite or link to this item: 10.1016/j.jdent.2024.105318
Title: Core outcomes measures in dental computer vision studies (DentalCOMS)
Authors: Büttner, Martha
Rokhshad, Rata
Brinz, Janet
Issa, Julien
Chaurasia, Akhilanand
Uribe, Sergio E.
Karteva, Teodora
Chala, Sanaa
Tichy, Antonin
Schwendicke, Falk
Department of Conservative Dentistry and Oral Health
Keywords: AI;Computer vision;Deep learning;Image analysis;Radiograph;3.2 Clinical medicine;1.1. Scientific article indexed in Web of Science and/or Scopus database;General Dentistry;SDG 3 - Good Health and Well-being
Issue Date: Nov-2024
Citation: Büttner , M , Rokhshad , R , Brinz , J , Issa , J , Chaurasia , A , Uribe , S E , Karteva , T , Chala , S , Tichy , A & Schwendicke , F 2024 , ' Core outcomes measures in dental computer vision studies (DentalCOMS) ' , Journal of Dentistry , vol. 150 , 105318 , pp. 1-7 . https://doi.org/10.1016/j.jdent.2024.105318
Abstract: Objectives: To improve reporting and comparability as well as to reduce bias in dental computer vision studies, we aimed to develop a Core Outcome Measures Set (COMS) for this field. The COMS was derived consensus based as part of the WHO/ITU/WIPO Global Initiative AI for Health (WHO/ITU/WIPO AI4H). Methods: We first assessed existing guidance documents of diagnostic accuracy studies and conducted interviews with experts in the field. The resulting list of outcome measures was mapped against computer vision modeling tasks, clinical fields and reporting levels. The resulting systematization focused on providing relevant outcome measures whilst retaining details for meta-research and technical replication, displaying recommendations towards (1) levels of reporting for different clinical fields and tasks, and (2) outcome measures. The COMS was consented using a 2-staged e-Delphi, with 26 participants from various IADR groups, the WHO/ITU/WIPO AI4H, ADEA and AAOMFR. Results: We assigned agreed levels of reporting to different computer vision tasks. We agreed that human expert assessment and diagnostic accuracy considerations are the only feasible method to achieve clinically meaningful evaluation levels. Studies should at least report on eight core outcome measures: confusion matrix, accuracy, sensitivity, specificity, precision, F-1 score, area-under-the-receiver-operating-characteristic-curve, and area-under-the-precision-recall-curve. Conclusion: Dental researchers should aim to report computer vision studies along the outlined COMS. Reviewers and editors may consider the defined COMS when assessing studies, and authors are recommended to justify when not employing the COMS. Clinical significance: Comparing and synthesizing dental computer vision studies is hampered by the variety of reported outcome measures. Adherence to the defined COMS is expected to increase comparability across studies, enable synthesis, and reduce selective reporting.
Description: Publisher Copyright: © 2024
DOI: 10.1016/j.jdent.2024.105318
ISSN: 0300-5712
Appears in Collections:Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure

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