Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study

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dc.contributor.authorOzsunkar, Pelin Senem
dc.contributor.authorOzen, Duygu CelIk
dc.contributor.authorAbdelkarim, Ahmed Z.
dc.contributor.authorDuman, Sacide
dc.contributor.authorUgurlu, Mehmet
dc.contributor.authorDemir, Mehmet Ridvan
dc.contributor.authorKuleli, Batuhan
dc.date.accessioned2024-08-04T20:55:56Z
dc.date.available2024-08-04T20:55:56Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 x 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.Conclusions The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm.Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.en_US
dc.identifier.doi10.1186/s12903-024-04262-1
dc.identifier.issn1472-6831
dc.identifier.issue1en_US
dc.identifier.pmid38658959en_US
dc.identifier.scopus2-s2.0-85191340335en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s12903-024-04262-1
dc.identifier.urihttps://hdl.handle.net/11616/101952
dc.identifier.volume24en_US
dc.identifier.wosWOS:001207746400003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Oral Healthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeep learningen_US
dc.subjectDentistryen_US
dc.subjectPhotographyen_US
dc.subjectWhite spot lesionsen_US
dc.titleDetecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot studyen_US
dc.typeArticleen_US

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