| Title: |
Deep Learning-Based Prediction Model for the Cobb Angle in Adolescent Idiopathic Scoliosis Patients |
| Authors: |
Chun-Sing (Elvis) Chui; Zhong He; Tsz-Ping Lam; Ka-Kwan (Kyle) Mak; Hin-Ting (Randy) Ng; Chun-Hai (Ericsson) Fung; Mei-Shuen Chan; Sheung-Wai Law; Yuk-Wai (Wayne) Lee; Lik-Hang (Alec) Hung; Chiu-Wing (Winnie) Chu; Sze-Yi (Sibyl) Mak; Wing-Fung (Edmond) Yau; Zhen Liu; Wu-Jun Li; Zezhang Zhu; Man Yeung (Ronald) Wong; Chun-Yiu (Jack) Cheng; Yong Qiu; Shu-Hang (Patrick) Yung |
| Source: |
Diagnostics, Vol 14, Iss 12, p 1263 (2024) |
| Publisher Information: |
MDPI AG |
| Publication Year: |
2024 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
adolescent idiopathic scoliosis (AIS); Cobb angles; Feedforward Neural Network (FNN); Medicine (General); R5-920 |
| Description: |
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 (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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://www.mdpi.com/2075-4418/14/12/1263; https://doaj.org/toc/2075-4418; https://doaj.org/article/9d4cc4386aed471fa0ee07c9d54c703f |
| DOI: |
10.3390/diagnostics14121263 |
| Availability: |
https://doi.org/10.3390/diagnostics14121263; https://doaj.org/article/9d4cc4386aed471fa0ee07c9d54c703f |
| Accession Number: |
edsbas.788C6686 |
| Database: |
BASE |