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Background <p>Differentiating acute from chronic wedge-shaped thoracolumbar vertebral deformities on conventional lateral lumbar radiographs remains clinically challenging, especially when osteoporosis status also needs to be considered. This study aimed to develop and evaluate a You Only Look Once (YOLO)v8n framework for vertebral-level detection and classification of thoracolumbar fractures with osteoporosis-related stratification on lateral lumbar radiographs.</p> Methods <p>We retrospectively collected 1352 lateral lumbar radiographs from 1352 patients, with one radiograph per patient. A total of 1774 vertebral fracture segments were manually annotated. Lumbar magnetic resonance imaging (MRI) and dual-energy X-ray absorptiometry (DXA) were used as reference standards to stratify vertebral targets into three categories: acute fracture with osteoporosis, acute fracture without osteoporosis and chronic fracture with osteoporosis. The dataset was divided into training and validation subsets at the patient level. A YOLOv8n detector was trained as the primary model. To strengthen methodological rigor, additional baseline comparison experiments were conducted under the same patient-level training/validation split using YOLOv5n and Faster R-CNN. Detection performance was assessed using precision, recall, F1-score, mean average precision (mAP) 50 and mAP50-95.</p> Results <p>On the validation set, the YOLOv8n model achieved a precision of 0.495, recall of 0.482, F1-score of 0.490, mAP50 of 0.506, and mAP50-95 of 0.397. In comparative experiments, YOLOv5n achieved a precision of 0.451, recall of 0.549, F1-score of 0.495, mAP50 of 0.494, and mAP50-95 of 0.367, whereas Faster R-CNN achieved a precision of 0.273, recall of 0.814, F1-score of 0.409, mAP50 of 0.300, and mAP50-95 of 0.217. These findings indicate that YOLOv8n provided the most balanced overall detection performance in the present dataset.</p> Conclusion <p>The proposed YOLOv8n framework demonstrated preliminary feasibility for automated vertebral-level detection and classification of thoracolumbar fractures with osteoporosis-related stratification on lateral lumbar radiographs. However, given the moderate overall performance and lack of external validation, the current model should be regarded as an assistive screening tool rather than a standalone diagnostic system.</p>

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Application of a YOLOv8-based model on lateral lumbar radiographs for screening of acute and chronic thoracolumbar fractures and osteoporosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Baisen Chen,
  • Yukang Cheng,
  • Jiaming Cui,
  • Chunyan Ji,
  • Yuyu Sun,
  • Zhiming Cui,
  • Guanhua Xu

摘要

Background

Differentiating acute from chronic wedge-shaped thoracolumbar vertebral deformities on conventional lateral lumbar radiographs remains clinically challenging, especially when osteoporosis status also needs to be considered. This study aimed to develop and evaluate a You Only Look Once (YOLO)v8n framework for vertebral-level detection and classification of thoracolumbar fractures with osteoporosis-related stratification on lateral lumbar radiographs.

Methods

We retrospectively collected 1352 lateral lumbar radiographs from 1352 patients, with one radiograph per patient. A total of 1774 vertebral fracture segments were manually annotated. Lumbar magnetic resonance imaging (MRI) and dual-energy X-ray absorptiometry (DXA) were used as reference standards to stratify vertebral targets into three categories: acute fracture with osteoporosis, acute fracture without osteoporosis and chronic fracture with osteoporosis. The dataset was divided into training and validation subsets at the patient level. A YOLOv8n detector was trained as the primary model. To strengthen methodological rigor, additional baseline comparison experiments were conducted under the same patient-level training/validation split using YOLOv5n and Faster R-CNN. Detection performance was assessed using precision, recall, F1-score, mean average precision (mAP) 50 and mAP50-95.

Results

On the validation set, the YOLOv8n model achieved a precision of 0.495, recall of 0.482, F1-score of 0.490, mAP50 of 0.506, and mAP50-95 of 0.397. In comparative experiments, YOLOv5n achieved a precision of 0.451, recall of 0.549, F1-score of 0.495, mAP50 of 0.494, and mAP50-95 of 0.367, whereas Faster R-CNN achieved a precision of 0.273, recall of 0.814, F1-score of 0.409, mAP50 of 0.300, and mAP50-95 of 0.217. These findings indicate that YOLOv8n provided the most balanced overall detection performance in the present dataset.

Conclusion

The proposed YOLOv8n framework demonstrated preliminary feasibility for automated vertebral-level detection and classification of thoracolumbar fractures with osteoporosis-related stratification on lateral lumbar radiographs. However, given the moderate overall performance and lack of external validation, the current model should be regarded as an assistive screening tool rather than a standalone diagnostic system.