Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images

Sharif, Naeha; Gilani, Syed Zulqarnain; Suter, David; Reid, Siobhan; Szulc, Paweł; Kimelman, Douglas; Monchka, Barret A.; Jozani, Mohammad Jafari; Hodgson, Jonathan M.; Sim, Marc; Zhu, Kun; Harvey, Nicholas C.; Kiel, Douglas P.; Prince, Richard; Schousboe, John T.; Leslie, William D.; Lewis, Joshua R.

doi:10.1016/j.ebiom.2023.104676

Cited by 7 publications

(19 citation statements)

References 20 publications

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“…Development of the ML approach used in this study (ML-AAC24) has also been detailed. [13] The following section provides a brief overview.…”

Section: Methodsmentioning

confidence: 99%

“…The ML-AAC24 scores used in the current study were obtained from our previous work focused on automated AAC-24 assessment for CVD outcomes, and the ML algorithm was only trained on expertly assessed AAC-24 scores. [13] A brief explanation of the training process is as follows [13] : a regression network was employed based on image features. This was based on the Kauppila AAC 24-point semiquantitative scoring method (AAC-24), which is the most widely used method to manually score AAC.…”

Section: Assessment Of Accmentioning

confidence: 99%

“…This was based on the Kauppila AAC 24-point semiquantitative scoring method (AAC-24), which is the most widely used method to manually score AAC. [13,18] Briefly, the framework consists of three modules: image preprocessing, feature extraction, and regression. The pretrained EfficientNet-B3 was used for feature extraction due to its efficiency and high performance.…”

Section: Assessment Of Accmentioning

confidence: 99%

“…To address this limitation, machine learning (ML) approaches have been investigated to automate the identification of AAC and AAC-24 scoring from lateral spine images. [12,13] This builds on work to automate measurement of aortic calcification from abdominal computed tomography (CT) scans. [14] We recently developed and tested a ML algorithm for the automated assessment of AAC (ML-AAC24) from DXA-derived lateral spine images acquired from various major manufacturers (and models) currently used clinically.…”

Section: Introductionmentioning

confidence: 99%

“…These scores were validated against future major adverse cardiovascular events, cardiovascular, and all-cause mortality from Hologic and GE machines. [13] Given that extensive AAC is associated with poorer musculoskeletal outcomes (e.g., falls [7] and fracture [8] ), determining whether these relationships remain comparable after automation (i.e., ML-AAC24) is a critical step to enhance the clinical utility of lateral spine images at the time of BMD measurement. Therefore, this study examined whether ML-AAC24 is related to longterm falls and fracture risk in community-dwelling older women, representing a high-risk population.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Dalla Via,

Gebre,

Smith

et al. 2023

Journal of Bone and Mineral Research

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Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long‐term prognosis. AAC can be assessed on dual‐energy X‐ray absorptiometry (DXA)‐derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24‐point scoring method (AAC‐24). Previous studies have identified robust associations between AAC‐24 score, incident falls and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine‐learning algorithm for assessing AAC‐24 scores (ML‐AAC24). In this prospective study, we evaluated the association between ML‐AAC24 and long‐term incident falls and fractures in 1,023 community‐dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow‐up, 253 (24.7%) women experienced a clinical fracture identified via self‐report every 4–6 months and verified by X‐ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML‐AAC24 ≥ 2) had a greater risk of clinical fractures (HR 1.42, 95%CI 1.10–1.85) and fall‐related hospitalization (HR 1.35, 95%CI 1.09–1.66), compared to those with low AAC (ML‐AAC24 ≤ 1). Similar to manually assessed AAC‐24, ML‐AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC‐24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long‐term incident clinical fractures and injurious falls. However, the performance of the ML‐AAC24 algorithm needs to be verified in independent cohorts.This article is protected by copyright. All rights reserved.

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“…Development of the ML approach used in this study (ML-AAC24) has also been detailed. [13] The following section provides a brief overview.…”

Section: Methodsmentioning

confidence: 99%

Section: Assessment Of Accmentioning

confidence: 99%

Section: Assessment Of Accmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Dalla Via,

Gebre,

Smith

et al. 2023

Journal of Bone and Mineral Research

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Costunolide mitigates inflammation and promotes extracellualr matrix integrity of thoracic aortic dissection by inhibiting NF-κB signaling

Han,

Tang,

Lin

et al. 2024

International Immunopharmacology

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Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment

Schousboe,

Lewis,

Monchka

et al. 2024

Journal of Bone and Mineral Research

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Whether simultaneous automated ascertainments of prevalent vertebral fracture (auto-PVFx) and abdominal aortic calcification (auto-AAC) on vertebral fracture assessment (VFA) lateral spine bone density (BMD) images jointly predict incident fractures in routine clinical practice is unclear. We estimated the independent associations of auto-PVFx and auto-AAC primarily with incident major osteoporotic and secondarily with incident hip and any clinical fractures in 11 013 individuals (mean [SD] age 75.8 [6.8] years, 93.3% female) who had a BMD test combined with VFA between March 2010 and December 2017. Auto-PVFx and auto-AAC were ascertained using convolutional neural networks (CNNs). Proportional hazards models were used to estimate the associations of auto-PVFx and auto-AAC with incident fractures over a mean (SD) follow-up of 3.7 (2.2) years, adjusted for each other and other risk factors. At baseline, 17% (n = 1881) had auto-PVFx and 27% (n = 2974) had a high level of auto-AAC (≥ 6 on scale of 0 to 24). Multivariable-adjusted hazard ratios (HR) for incident major osteoporotic fracture (95% C.I.) were 1.85 (1.59, 2.15) for those with compared to those without auto-PVFx, and 1.36 (1.14, 1.62) for those with high compared to low auto-AAC. The multivariable-adjusted HRs for incident hip fracture were 1.62 (95% C.I. 1.26 to 2.07) for those with compared to those without auto-PVFx, and 1.55 (95% C.I. 1.15 to 2.09) for those high auto-AAC compared to low auto-AAC. The 5-year cumulative incidence of major osteoporotic fracture was 7.1% in those with no auto-PVFx and low auto-AAC, 10.1% in those with no auto-PVFx and high auto-AAC, 13.4% in those with auto-PVFx and low auto-AAC, and 18.0% in those with auto-PVFx and high auto-AAC. While physician manual review of images in clinical practice will still be needed to confirm image quality and provide clinical context for interpretation, simultaneous automated ascertainment of auto-PVFx and auto-AAC can aid fracture risk assessment.

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Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images

Cited by 7 publications

References 20 publications

Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women

Costunolide mitigates inflammation and promotes extracellualr matrix integrity of thoracic aortic dissection by inhibiting NF-κB signaling

Simultaneous automated ascertainment of prevalent vertebral fracture and abdominal aortic calcification in clinical practice: role in fracture risk assessment

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