Opportunistic Screening for Osteoporosis Using CT Scans of the Knee: A Pilot Study

Elmahdy, Mahmoud; Sebro, Ronnie

doi:10.3233/shti230305

Cited by 2 publications

(2 citation statements)

References 2 publications

(9 reference statements)

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“…Furthermore, non-spinal skeletal sites may be subjected to fewer degenerative changes [170], enhancing their predictive value for BMD and future fracture risk. The sites evaluated in the literature include the hip, distal femur [129], tibial, fibula [94], distal radius, hand bones [137], ribs, sternum, and clavicle [127]. This broader assessment can be particularly useful in cases in which spine imaging is not performed or when it will not provide a complete picture of the patient's bone health, e.g., vertebral degeneration, fractures, or instrumentation precludes accurate assessment.…”

Section: Technical Considerations: Areas Sampledmentioning

confidence: 99%

“…These applications encompass osteoporosis risk prediction [83][84][85] and fracture risk assessment [86][87][88][89]. Notably, AI has been employed to streamline the complex processing of CT images and enhance automated segmentation [90][91][92], which has been utilized for BMD measurement and classification in multiple studies [93][94][95][96][97][98][99][100][101][102][103]. Radiomics involves the extraction and analysis of a large number of quantitative features from medical images, such as CT scans.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong,

Liu,

Makmur

et al. 2023

Bioengineering

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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.

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Section: Technical Considerations: Areas Sampledmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong,

Liu,

Makmur

et al. 2023

Bioengineering

View full text Add to dashboard Cite

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GLCM-Based FBLS: A Novel Broad Learning System for Knee Osteopenia and Osteoprosis Screening in Athletes

Chen,

Zheng,

Duan

et al. 2023

Applied Sciences

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Due to the physical strain experienced during intense workouts, athletes are at a heightened risk of developing osteopenia and osteoporosis. These conditions not only impact their overall health but also their athletic performance. The current clinical screening methods for osteoporosis are limited by their high radiation dose, complex post-processing requirements, and the significant time and resources needed for implementation. This makes it challenging to incorporate them into athletes’ daily training routines. Consequently, our objective was to develop an innovative automated screening approach for detecting osteopenia and osteoporosis using X-ray image data. Although several automated screening methods based on deep learning have achieved notable results, they often suffer from overfitting and inadequate datasets. To address these limitations, we proposed a novel model called the GLCM-based fuzzy broad learning system (GLCM-based FBLS). Initially, texture features of X-ray images were extracted using the gray-level co-occurrence matrix (GLCM). Subsequently, these features were combined with the fuzzy broad learning system to extract crucial information and enhance the accuracy of predicting osteoporotic conditions. Finally, we applied the proposed method to the field of osteopenia and osteoporosis screening. By comparing this model with three advanced deep learning models, we have verified the effectiveness of GLCM-based FBLS in the automatic screening of osteoporosis for athletes.

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Opportunistic Screening for Osteoporosis Using CT Scans of the Knee: A Pilot Study

Cited by 2 publications

References 2 publications

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

GLCM-Based FBLS: A Novel Broad Learning System for Knee Osteopenia and Osteoprosis Screening in Athletes

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