Dubuc Antoine, Zitouni Anissa, Thomas Charlotte, Kémoun Philippe, Cousty Sarah, Monsarrat Paul, Laurencin Sara
School of Dental Medicine and CHU de Toulouse-Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France.
Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France.
J Clin Med. 2022 Nov 7;11(21):6596. doi: 10.3390/jcm11216596.
Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.
尽管人工智能在皮肤皮肤病诊断中的应用正在蓬勃发展，但在口腔病理学中的应用仍有待开发。早期诊断以及因此的早期管理，仍然是口腔黏膜癌成功管理的关键点。目的是开发和评估一种能够预测口腔黏膜病变诊断的机器学习算法。这项队列研究纳入了2015年1月至2020年12月在图卢兹大学医院口腔黏膜病理会诊中接受随访的患者。从每位患者收集照片、人口统计学和医学数据以构成临床病例。然后开发并优化了一种机器学习模型，并与该领域经典使用的5种模型进行比较。共有299名患者代表1242条口腔黏膜病变记录用于训练和评估机器学习模型。我们的模型在诊断预测方面的平均准确率达到了0.84。特异性和敏感性范围分别为0.89至1.00和0.72至0.92。事实证明，其他模型在执行这项任务时效率较低。这些结果表明基于机器学习的工具在高精度诊断口腔黏膜病变方面的实用性。此外，这项研究的结果证实，除了病变本身之外，考虑临床数据和病史似乎也起着重要作用。
