Diagnostics (Basel, Switzerland) · 2024
Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients.
СтатьяНазвание и аннотация: Перевод в очереди
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Chui Chun-Sing Elvis, He Zhong, Lam Tsz-Ping, Mak Ka-Kwan Kyle, Ng Hin-Ting Randy, Fung Chun-Hai Ericsson, Chan Mei-Shuen, Law Sheung-Wai, Lee Yuk-Wai Wayne, Hung Lik-Hang Alec, Chu Chiu-Wing Winnie, Mak Sze-Yi Sibyl, Yau Wing-Fung Edmond, Liu Zhen, Li Wu-Jun, Zhu Zezhang, Wong Man Yeung Ronald, Cheng Chun-Yiu Jack, Qiu Yong, Yung Shu-Hang Patrick
Аннотация
Scoliosis, characterized by spine deformity, is most common in adolescent idiopathic scoliosis (AIS). Manual Cobb angle measurement limitations underscore the need for automated tools. This study employed a vertebral landmark extraction method and Feedforward Neural Network (FNN) to predict scoliosis progression in 79 AIS patients. The novel intervertebral angles matrix format showcased results. The mean absolute error for the intervertebral angle progression was 1.5 degrees, while the Pearson correlation of the predicted Cobb angles was 0.86. The accuracy in classifying Cobb angles (<15°, 15-25°, 25-35°, 35-45°, >45°) was 0.85, with 0.65 sensitivity and 0.91 specificity. The FNN demonstrated superior accuracy, sensitivity, and specificity, aiding in tailored treatments for potential scoliosis progression. Addressing FNNs' over-fitting issue through strategies like "dropout" or regularization could further enhance their performance. This study presents a promising step towards automated scoliosis diagnosis and prognosis.