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利用智能手机摄像头通过人工智能诊断扁平足和高弓足

Utilization of artificial intelligence in the diagnosis of pes planus and pes cavus with a smartphone camera.

作者信息

Ghandour Samir, Lebedev Anton, Tung Wei-Shao, Semianov Konstantin, Semjanow Artem, DiGiovanni Christopher W, Ashkani-Esfahani Soheil, Pineda Lorena Bejarano

机构信息

Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.

Department of Orthopaedics, Massachusetts General Hospital, Boston, MA 02114, United States.

出版信息

World J Orthop. 2024 Dec 18;15(12):1146-1154. doi: 10.5312/wjo.v15.i12.1146.

Abstract

BACKGROUND

Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.

AIM

To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.

METHODS

An algorithm that integrated a deep convolutional neural network (CNN) into a smartphone camera was utilized to detect pes planus and pes cavus deformities. This case control study was conducted at a tertiary hospital with participants recruited from two orthopaedic foot and ankle clinics. The CNN was trained and tested using photographs of the medial aspect of participants' feet, taken under standardized conditions. Participants included subjects with standard foot alignment, pes planus, or pes cavus determined by an expert clinician using the foot posture index. The model's performance was assessed in comparison to clinical assessment and radiographic measurements, specifically lateral tarsal-first metatarsal angle and calcaneal inclination angle.

RESULTS

The CNN model demonstrated high accuracy in diagnosing both pes planus and pes cavus, with an optimized area under the curve of 0.90 for pes planus and 0.90 for pes cavus. It showed a specificity and sensitivity of 84% and 87% for pes planus detection, respectively; and 97% and 70% for pes cavus, respectively. The model's prediction correlated moderately with radiographic lateral Meary's angle measurements, indicating the model's excellent reliability in assessing food arch deformity ( < 0.05).

CONCLUSION

This study highlights the potential of using a smartphone-based CNN model as a screening tool that is reliable and accessible for the detection of pes planus and pes cavus deformities, which is especially beneficial for underserved communities and patients with pain generated by subtle foot arch deformities.

摘要

背景

扁平足和高弓足是常见的足部畸形,通常需要临床和影像学评估来进行诊断及后续可能的治疗。传统的诊断方法虽然有效,但存在成本、辐射暴露和可及性等局限性,尤其是在医疗服务不足的地区。

目的

开发利用智能手机摄像头检测和分类此类畸形的深度学习算法。

方法

采用一种将深度卷积神经网络(CNN)集成到智能手机摄像头中的算法来检测扁平足和高弓足畸形。本病例对照研究在一家三级医院进行,参与者从两个骨科足踝诊所招募。使用在标准化条件下拍摄的参与者足部内侧照片对CNN进行训练和测试。参与者包括经专家临床医生使用足姿势指数确定为标准足排列、扁平足或高弓足的受试者。将该模型的性能与临床评估和影像学测量结果进行比较,具体为外侧跗骨 - 第一跖骨角和跟骨倾斜角。

结果

CNN模型在诊断扁平足和高弓足方面均显示出高准确性,扁平足的优化曲线下面积为0.90,高弓足为0.90。其在检测扁平足时的特异性和敏感性分别为84%和87%;检测高弓足时分别为97%和70%。该模型的预测与影像学外侧梅里角测量结果中度相关,表明该模型在评估足弓畸形方面具有出色的可靠性(<0.05)。

结论

本研究强调了使用基于智能手机的CNN模型作为一种筛查工具的潜力,该工具可靠且可及,可用于检测扁平足和高弓足畸形,这对医疗服务不足的社区以及因细微足弓畸形产生疼痛的患者尤其有益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3aba/11686530/2a6eba74380c/WJO-15-1146-g001.jpg

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