Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.

IF 2.9 3区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Oral diseases Pub Date : 2024-11-03 DOI:10.1111/odi.15188

Olga Di Fede, Gaetano La Mantia, Marco Parola, Laura Maniscalco, Domenica Matranga, Pietro Tozzo, Giuseppina Campisi, Mario G C A Cimino

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引用次数: 0

Abstract

Objective: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta-analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.

Materials and methods: A scoping review was conducted to identify relevant studies published in the last 5 years (2018-2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus. Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta-analysis was conducted to synthesize the findings.

Results: Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta-analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80-0.91) and 0.67 (95% CI = 0.58-0.75), respectively.

Conclusions: The results of meta-analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.

Trial registration: Open Science Framework (https://osf.io/4n8sm).

查看原文本刊更多论文

利用深度学习自动检测口腔恶性病变：范围综述与元分析》。

目的：口腔疾病，特别是恶性病变，是全球严重的健康问题，需要早期诊断才能有效治疗。近年来，深度学习（DL）已成为自动检测和分类口腔病变的有力工具。本研究通过进行范围综述和荟萃分析，旨在概述利用深度学习自动检测口腔病变领域的进展和成就：进行了范围综述，以确定过去 5 年（2018-2023 年）发表的相关研究。使用多个电子数据库（包括 PubMed、Web of Science 和 Scopus）进行了全面检索。两名审稿人独立评估了研究的资格，并使用标准化表格提取数据，然后进行荟萃分析，对研究结果进行综合：结果：共发现并纳入了 14 项利用各种 DL 算法从临床图像中检测和分类口腔病变的研究。其中三项被纳入荟萃分析。估计的集合灵敏度和特异度分别为 0.86（95% 置信区间 [CI] = 0.80-0.91）和 0.67（95% CI = 0.58-0.75）：荟萃分析结果表明，DL 算法可提高口腔病变的诊断率。未来的研究应开发用于自动诊断的有效算法：开放科学框架（https://osf.io/4n8sm）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Oral diseases 医学-牙科与口腔外科

CiteScore

7.60

自引率

5.30%

发文量

325

审稿时长

4-8 weeks

期刊介绍： Oral Diseases is a multidisciplinary and international journal with a focus on head and neck disorders, edited by leaders in the field, Professor Giovanni Lodi (Editor-in-Chief, Milan, Italy), Professor Stefano Petti (Deputy Editor, Rome, Italy) and Associate Professor Gulshan Sunavala-Dossabhoy (Deputy Editor, Shreveport, LA, USA). The journal is pre-eminent in oral medicine. Oral Diseases specifically strives to link often-isolated areas of dentistry and medicine through broad-based scholarship that includes well-designed and controlled clinical research, analytical epidemiology, and the translation of basic science in pre-clinical studies. The journal typically publishes articles relevant to many related medical specialties including especially dermatology, gastroenterology, hematology, immunology, infectious diseases, neuropsychiatry, oncology and otolaryngology. The essential requirement is that all submitted research is hypothesis-driven, with significant positive and negative results both welcomed. Equal publication emphasis is placed on etiology, pathogenesis, diagnosis, prevention and treatment.