Application of intelligent X-ray image analysis in risk assessment of osteoporotic fracture of femoral neck in the elderly

Development and reporting of artificial intelligence in osteoporosis management

An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

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

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.

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

OBJECTIVES

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

METHODS

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.

RESULTS

Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.

CONCLUSIONS

ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.

PROSPERO REGISTRATION NUMBER

CRD42022346896.