Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29^th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

中文翻译：

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牙科和口腔颌面疼痛管理中的机器学习和智能诊断：系统综述

目的。这项研究探索了机器学习在诊断（1）牙齿疾病，（2）牙周疾病，（3）创伤和神经痛，（4）囊肿和肿瘤，（5）腺体的临床影响，有效性，局限性和人类比较结果疾病，以及（6）骨骼和颞下颌关节可能是造成牙齿和口面部疼痛的原因。方法。Scopus，PubMed和Web of Science（所有数据库）均由2位审阅者进行搜索，直到29^日2020年10月。根据PRISMA-DTA指南，根据预定义的资格标准对文章进行筛选和叙述性综合。使用MI-CLAIM清单对与人类临床医生进行直接参考测试比较的文章进行了评估。通过JBI-DTA严格评估评估了偏倚的风险，并使用GRADE方法评估了证据的确定性。提取了有关牙齿疼痛和疾病的量化方法，机器学习中训练和测试数据队列的条件特征，诊断结果以及与临床医生进行诊断测试比较（如果适用）的信息。结果。共找到34篇符合条件的文章进行数据合成，其中8篇文章直接与人类临床医生进行了参考比较。在MI-CLAIM方法中，有7篇论文的得分超过了13分（满分为15分），而在JBI-DTA评估中，所有论文的得分均在5分以上（满分7分）。GRADE方法揭示了严重偏差和不一致的风险，为了促进机器学习，大多数研究包含的确诊案例要比其真实患病率高。通常发现，患者的症状和临床病史在训练精确的机器学习模型方面不如X线照片或组织学可靠。建议在训练模型的临床医生之间达成较低的协议水平，这会对预测准确性产生负面影响。结论。牙科和口腔保健中的机器学习在诊断具有症状性疼痛的疾病以及改进的未来迭代方法方面已显示出可观的结果，并且可以在临床中用作诊断辅助工具。当前的综述没有内部分析机器学习模型及其各自的算法，也没有考虑造成口腔疼痛的混杂变量和因素。