Integration of a vertebral fracture identification service into a fracture liaison service: a quality improvement project

Ong, Terence; Copeland, Richard; Thiam, Chiann Ni; Mas, Georgina Cerdà; Marshall, Lindsey; Sahota, Opinder

doi:10.1007/s00198-020-05710-8

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…This could be done by developing a clinical decision tool to help the FLS team to identify which patients to refer for further assessment and treatment based on their risk of dying and the predicted benefit of attending an FLS. Such a tool has previously been developed by Ong et al [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting

Bendtsen,

Hitz

2024

Calcif Tissue Int

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This study evaluated the performance of a vertebral fracture detection algorithm (HealthVCF) in a real-life setting and assessed the impact on treatment and diagnostic workflow. HealthVCF was used to identify moderate and severe vertebral compression fractures (VCF) at a Danish hospital. Around 10,000 CT scans were processed by the HealthVCF and CT scans positive for VCF formed both the baseline and 6-months follow-up cohort. To determine performance of the algorithm 1000 CT scans were evaluated by specialized radiographers to determine performance of the algorithm. Sensitivity was 0.68 (CI 0.581–0.776) and specificity 0.91 (CI 0.89–0.928). At 6-months follow-up, 18% of the 538 patients in the retrospective cohort were dead, 78 patients had been referred for a DXA scan, while 25 patients had been diagnosed with osteoporosis. A higher mortality rate was seen in patients not known with osteoporosis at baseline compared to patients known with osteoporosis at baseline, 12.8% versus 22.6% (p = 0.003). Patients receiving bisphosphonates had a lower mortality rate (9.6%) compared to the rest of the population (20.9%) (p = 0.003). HealthVCF demonstrated a poorer performance than expected, and the tested version is not generalizable to the Danish population. Based on its specificity, the HealthVCF can be used as a tool to prioritize resources in opportunistic identification of VCF’s. Implementing such a tool on its own only resulted in a small number of new diagnoses of osteoporosis and referrals to DXA scans during a 6-month follow-up period. To increase efficiency, the HealthVCF should be integrated with Fracture Liaison Services (FLS).

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Section: Discussionmentioning

confidence: 99%

Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting

Bendtsen,

Hitz

2024

Calcif Tissue Int

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“…Machine learning has previously been applied to a number of other neurological diagnostic targets. Several models for detecting spine fractures on CT have been developed and continue to be integrated into clinical workflows 10–12. Models for identifying and classifying epidural hematomas in the brain on CT have also been developed 13–15.…”

Section: Discussionmentioning

confidence: 99%

“…Several models for detecting spine fractures on CT have been developed and continue to be integrated into clinical workflows. [10][11][12] Models for identifying and classifying epidural hematomas in the brain on CT have also been developed. [13][14][15] However, to our knowledge, this is the first machine learning model that has been developed for detection of spinal epidural hematomas and abscesses on CT.…”

Section: Discussionmentioning

confidence: 99%

Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning

Harris

Baginski

Bronstein

et al. 2022

Spine

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Background. Critical spinal epidural pathologies can cause paralysis or death if untreated. Although magnetic resonance imaging is the preferred modality for visualizing these pathologies, computed tomography (CT) occurs far more commonly than magnetic resonance imaging in the clinical setting. Objective. A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. This model has utility for both worklist prioritization of emergent studies and identifying missed findings. Materials and Methods. There were 153 studies with epidural lesions available for training. These lesions were segmented and used to train a machine learning model. A test data set was also created using previously missed epidural lesions. The trained model was then integrated into a teleradiology workflow for 90 days. Studies were sent to secondary manual review if the model detected an epidural lesion but none was mentioned in the clinical report. Results. The model correctly identified 50.0% of epidural lesions in the test data set with 99.0% specificity. For prospective data, the model correctly prioritized 66.7% of the 18 epidural lesions diagnosed on the initial read with 98.9% specificity. There were 2.0 studies flagged for potential missed findings per day, and 17 missed epidural lesions were found during a 90-day time period. These results suggest almost half of critical spinal epidural lesions visible on CT imaging are being missed on initial diagnosis. Conclusion. A machine learning model for identifying spinal epidural hematomas and abscesses on CT can be implemented in a clinical workflow.

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“…The algorithm showed an AUC of 0.94, making it highly effective in opportunistically identifying vertebral fractures in routine CT scans [19]. Ong et al evaluated the efficacy of a machine learning algorithm in identifying vertebral fractures from CT scans, revealing that the algorithm detected fractures in 19.1% of 4461 patients, outperforming hospital radiologists whose reports only mentioned 49% of these fractures [20]. Valentinitsch et al used a random forest classifier and 3D texture features for opportunistic osteoporosis screening in thoracolumbar spine multi-detector CT scans, demonstrating high discriminatory power (AUC = 0.88) and outperforming global vertebral bone mineral density (vBMD) alone in identifying vertebral fractures [28].…”

Section: Opportunistic Screening and Fracture Liaisonmentioning

confidence: 99%

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

Maki,

Furuya,

Inoue

et al. 2024

JCM

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Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

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Integration of a vertebral fracture identification service into a fracture liaison service: a quality improvement project

Cited by 13 publications

References 16 publications

Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting

Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting

Detection of Critical Spinal Epidural Lesions on CT Using Machine Learning

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

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