Prediction of bone mineral density in CT using deep learning with explainability

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.

Influence of psychostimulants on bone mineral density and content among children with attention deficit hyperactivity disorder. A systematic review

There is a controversy over the influence of psychostimulant medications on bone mineral density (BMD) and bone mineral content (BMC) among children with attention-deficit-hyperactivity-disorder (ADHD). The aim of the present systematic review was to assess the influence of psychostimulant medications on BMD and BMC among children with ADHD. A comprehensive search of electronic databases, including PubMed, Scopus, Embase, and Cochrane Library, was conducted to identify relevant studies published up until July 2023. Clinical studies that addressed the focused question "Do psychostimulant medications affect bone mineral density and content in children with ADHD?" were included. Letters to the Editor, studies on animal-models, ex-vivo and in-vitro studies, commentaries and reviews were excluded. The primary outcome measures were changes in BMD and BMC. Study quality was assessed using the risk of bias for non-randomized studies-exposure tool. Five non-randomized clinical studies were included. The number of participants ranged from 18 to 6489 with mean ages ranging from 7.3 to 13.75 years. The study durations ranged between five and seven years. In all studies osseous evaluation was done using dual-energy X-ray absorptiometry. The bone locations examined included total body, lumbar-spine, femur, femoral-neck, femoral body, and pelvis. Two studies reported that psychostimulant medications reduce BMC and BMD. In one study, bone turnover, serum leptin and fat levels were reduced in children using psychostimulant medications but no unusual reduction recorded among controls. In general, 80 % of the studies concluded that psychostimulant medications compromise BMC and BMD. Power analysis was done in one study. One study had a low RoB and the remaining demonstrated some concerns. Given the methodological concerns observed in the included studies, arriving at a definitive conclusion regarding the effects of psychostimulant medications on BMC, BMD, and bone turnover in children with ADHD is challenging. However, it is important to acknowledge that an association between psychostimulant medications and these bone-related parameters cannot be disregarded.

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images

Background

The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns.

Methods

The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed.

Findings

The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort.

Interpretation

Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.