新型机器学习算法用于预测患有III类缺陷症的成年患者的治疗决策
Samim Taraji1, Salih Furkan Atici2, Grace Viana3
1Resident, Department of Orthodontics, College of Dentistry, University of Illinois Chicago.
概括
机器学习准确地预测了III类缺陷治疗,区分了伪装和手术. 这种人工智能方法有助于正牙诊断和治疗计划,减少了临床医生的主观性.
科学领域:
- 牙科和正牙科 牙科和正牙科
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 第三类 (Cl III) 牙骨缺陷的治疗是复杂的,通常依赖于专家的临床判断.
- 识别关键的头测量特征对于成年Cl III病例的有效治疗规划至关重要.
研究的目的:
- 为了确定在青春期后的III类恶性闭塞患者的关键形态特征.
- 评估机器学习 (ML) 算法的预测准确性,用于成人III类恶性闭塞的治疗计划.
主要方法:
- 追溯分析了182名成年III类异常闭塞患者的病历和治疗前侧脑图 (2003-2020年).
- 提取了40个头度特征,并用于训练8个ML模型.
- 模型的预测准确度,灵敏度和特异性被评估,极端梯度增强 (EGB) 和人工神经网络 (ANN) 显示出高性能.
主要成果:
- 在特征优化后,EGB模型在预测最佳治疗方法 (伪装与手术) 中达到93%的准确性.
- 通过EGB模型识别的关键预测因素包括Wit的评估,前部超喷射和Mx/Md比率.
- 在伪装和外科手术组之间没有观察到显著的人口差异.
结论:
- 在需要伪装与需要手术治疗的III类成年人之间存在显著的脑力测量差异.
- 具有93%准确度的AI模型可以协助正牙诊断和治疗规划,在复杂的病例中潜在地减少临床医生的主观性.
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