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

Autor: Kim C; Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Chuncheon, Korea., Kang M; Division of Software, Hallym University, Chuncheon, Korea., Yuh WT; Department of Neurosurgery, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, 7 Keunjaebong-gil, Hwaseong, Korea., Lee SL; Division of Software, Hallym University, Chuncheon, Korea., Lee JJ; Department of Anesthesiology, Chuncheon Sacred Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Chuncheon, Korea., Hou JU; Division of Software, Hallym University, Chuncheon, Korea. juhou@hallym.ac.kr., Kang SH; Department of Neurosurgery, Chuncheon Sacred Heart Hospital, Hallym University Medical Center, Hallym University College of Medicine, Chuncheon, Korea. nscharisma@hanmail.net.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 18; Vol. 14 (1), pp. 28388. Date of Electronic Publication: 2024 Nov 18.
DOI: 10.1038/s41598-024-79610-w
Abstrakt: 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.
Competing Interests: Declarations Competing interests The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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