Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs  

作　　者：Konrad Kwolek Artur Gądek Kamil Kwolek Radek Kolecki Henryk Liszka 

机构地区：[1]Department of Orthopedics and Traumatology,University Hospital,Kraków 30-688,Małopolska,Poland [2]Department of Orthopedics and Physiotherapy,Jagiellonian University Collegium Medicum,Kraków 30-688,Małopolska,Poland [3]Department of Spine Disorders and Orthopedics,Gruca Orthopedic and Trauma Teaching Hospital,Otwock 05-400,Poland

出　　处：《World Journal of Orthopedics》2023年第11期800-812,共13页世界骨科杂志（英文版）

摘　　要：BACKGROUND Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.AIM To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.METHODS The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs.181 radiographs were utilized for training(161)and validating(20)a U-Net neural network to achieve a mean Sørensen–Dice index>97%on bone segmentation.84 test radiographs were used for manual(computer assisted)and automated measurements of hallux valgus severity determined by hallux valgus(HVA)and intermetatarsal angles(IMA).The reliability of manual and computerbased measurements was calculated using the interclass correlation coefficient(ICC)and standard error of measurement(SEM).Inter-and intraobserver reliability coefficients were also compared.An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.RESULTS Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians.For HVA,the ICC between manual measurements was 0.96-0.99.For IMA,ICC was 0.78-0.95.Comparing manual against automated computer measurement,the reliability was high as well.For HVA,absolute agreement ICC and consistency ICC were 0.97,and SEM was 0.32.For IMA,absolute agreement ICC was 0.75,consistency ICC was 0.89,and SEM was 0.21.Additionally,a strong correlation(0.80)was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus,according to an operative treatment algorithm proposed by EFORT.CONCLUSION The proposed automated,artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential

关 键 词：Computer-aided diagnosis Artificial intelligence in orthopedics Automated preoperative decision support Deep learning Medical imaging 

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]