Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture

• Published in: Scientific reports 2024-11, Vol.14 (1), p.28388-9, Article 28388
• Main Authors: Kim, Chulho, Kang, Minjae, Yuh, Woon Tak, Lee, Seung-Lee, Lee, Jae Jun, Hou, Jong-Uk, Kang, Suk Hyung
• Format: Article
• Language: English
• Subjects: 631/1647/794; 692/699/578; 692/700; Aged; Aged, 80 and over; Algorithms; Anteroposterior; Compression; Datasets; Deep Learning; Diagnostic accuracy; Female; Fractures, Compression - diagnostic imaging; Humanities and Social Sciences; Humans; Machine learning in radiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; multidisciplinary; Osteoporosis; Osteoporotic Fractures - diagnostic imaging; Osteoporotic vertebral compression fractures; Retrospective Studies; ROC Curve; Science; Science (multidisciplinary); Spinal Fractures - diagnostic imaging; Training; Vertebrae; X-ray imaging; X-Rays
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-79610-w

Magnetic resonance imaging remains the gold standard for diagnosing osteoporotic vertebral compression fractures (OVCF), but the use of X-ray imaging, particularly anteroposterior (AP) and lateral views, is prevalent due to its accessibility and cost-effectiveness. We aim to assess whether the performance of AP images-based deep learning is comparable compared to those using lateral images. This retrospective study analyzed X-ray images from two tertiary teaching hospitals, involving 1,507 patients for the training and internal test, and 104 patients for the external test. The EfficientNet-B5-based algorithms were employed to classify OVCF and non-OVCF group. The model was trained with a 1:1 balanced dataset and validated through 5-fold cross validation. Performance outcomes were compared with the area under receiver operating characteristic (AUROC) curve. Out of a total of 1,507 patients, 799 were included in the training dataset and 708 were included in the internal test dataset. The training and internal test datasets were matched 1:1 as OVCF and non-OVCF patients. The DL model showed comparable classifying performance with internal test data ( N  = 708, AUROC for AP, 0.915; AUROC for lateral, 0.953) and external test data ( N  = 104, AUROC for AP, 0.982; AUROC for lateral, 0979), respectively. The other performances including F1 score and accuracy were also comparable. Especially, The AUROC of AP and lateral x-ray image-based DL was not significantly different ( p for DeLong test = 0.604). The EfficientNet-B5 algorithms using AP X-ray images shows comparable efficacy for classifying OVCF and non-OVCF compared to lateral images.