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

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

机构地区：[1]Department of Orthopaedic Surgery,Massachusetts General Hospital,Boston,MA 02114,United States [2]Department of Orthopaedics,Massachusetts General Hospital,Boston,MA 02114,United States

出　　处：《World Journal of Orthopedics》2024年第12期1146-1154,共9页世界骨科杂志（英文）

摘　　要：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(P<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.

关 键 词：Artificial intelligence Computer vision Three-dimensional scanning Lower extremity High-arch FLATFOOT 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] R684.3[医药卫生—骨科学]