Deep learning-based algorithms to detect vertebral fractures and osteoporosis using lateral spine X-ray radiograph

Hong, Namki; Cho, Sang Wouk; Shin, Sungjae; Jang, Seul A.; Roh, Sungwon; Rhee, Yumie; Kim, Hwiyoung; Kim, Kyoung Min; Cummings, Steven R.

doi:10.1016/j.bonr.2022.101576

Cited by 5 publications

(9 citation statements)

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“…All included studies were published between 2018 and 2023, with most being published after 2020. X-ray (17/40) [14][15][16][17][18][19][20][21][22][23][24][25] and CT (16/ 40) [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] images were the most frequently used modalities; digital projection radiography (DPR) [42][43][44] was used in three studies and MRI 45,46 in two studies. Two studies 47,48 using vertebral fracture assessment (VFA) images were considered in this review.…”

Section: Search Resultsmentioning

confidence: 99%

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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Objective Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. Methods We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. Results A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). Conclusions Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

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Section: Search Resultsmentioning

confidence: 99%

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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“…These studies commonly aimed to detect the presence of fractures using expert opinions for validating AI model outputs. Noteworthy contributions include Hong N et al 20 , who utilized a qualitative algorithm to classify vertebral fractures, with large datasets allowing robust comparisons across different scoring systems like the VERTE-X pVF and VERTE-X osteo scores. Similarly, Yilmaz EB et al [30] [31] and Monchka BA et al [32][33] employed convolutional neural networks and a modi ed algorithmbased qualitative approach, respectively, to classify fractures, focusing on binary outcomes-either 'fracture' or 'no fracture'.…”

Section: Diagnosis and Classi Cation Of Vertebral Fracturesmentioning

confidence: 99%

“…The forest plot [20][51] [52][53] [30][31] (Fig. 7) presents a comparative analysis of machine learning models based on their AUROC values for predicting speci c outcomes.…”

Section: Diagnosis/classi Cation Of Osteoporotic Vertebral Fracturesmentioning

confidence: 99%

Artificial Intelligence and Machine Learning for Risk Prediction and Diagnosis of Vertebral Fractures: A Systematic Review and Meta-Analysis

Namireddy,

Gill,

Peerbhai

et al. 2024

Preprint

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Introduction: With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. Method A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. Results AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. Conclusion AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

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“…(18) Several studies have used deep convolutional neural networks (DCNNs) for the recognition of vertebral fractures on X-ray images, but they were only able to make a determination of whether a fracture occurred in vertebrae and could not diagnose the specific fraction of the fracture. (19,20) spine radiographs from 300 subjects to diagnose fractures using DCNN, but the algorithm could only determine the presence of a fracture without the capability to identify its grade. (21) Similarly, Lindsey et al used deep neural networks for wrist fracture recognition using image semantic segmentation and regression tasks (the algorithm was designed for wrist fractures).…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs

Shen

Gao

et al. 2023

J of Bone & Mineral Res

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Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep‐learning‐based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X‐ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

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Deep learning-based algorithms to detect vertebral fractures and osteoporosis using lateral spine X-ray radiograph

Cited by 5 publications

References 0 publications

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

Artificial Intelligence and Machine Learning for Risk Prediction and Diagnosis of Vertebral Fractures: A Systematic Review and Meta-Analysis

Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs

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