1
|
Sharif N, Gilani SZ, Suter D, Reid S, Szulc P, Kimelman D, Monchka BA, Jozani MJ, Hodgson JM, Sim M, Zhu K, Harvey NC, Kiel DP, Prince RL, Schousboe JT, Leslie WD, Lewis JR. Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images. EBioMedicine 2023; 94:104676. [PMID: 37442671 PMCID: PMC10435763 DOI: 10.1016/j.ebiom.2023.104676] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training. METHODS Training and testing of model performance of the convolutional neural network (CNN) algorithm for automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines), with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial infarction or ischemic cerebrovascular disease ascertained from linked healthcare data. FINDINGS The average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012 images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate 14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment, moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31-1.80 & HR 2.06, 95% CI 1.75-2.42, respectively), compared to those with low ML-AAC-24. INTERPRETATION The ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be readily implemented into these clinical settings to improve identification of people at high CVD risk. FUNDING The study was supported by a National Health and Medical Research Council of Australia Ideas grant and the Rady Innovation Fund, Rady Faculty of Health Sciences, University of Manitoba.
Collapse
Affiliation(s)
- Naeha Sharif
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Centre for AI&ML, School of Science, Edith Cowan University, Perth, Australia; Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
| | - Syed Zulqarnain Gilani
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Centre for AI&ML, School of Science, Edith Cowan University, Perth, Australia
| | - David Suter
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Centre for AI&ML, School of Science, Edith Cowan University, Perth, Australia
| | - Siobhan Reid
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada
| | - Pawel Szulc
- INSERM UMR 1033, University of Lyon, Hospices Civils de Lyon, Lyon, France
| | - Douglas Kimelman
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Barret A Monchka
- George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Canada
| | | | - Jonathan M Hodgson
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia
| | - Marc Sim
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia
| | - Kun Zhu
- Medical School, The University of Western Australia, Perth, Australia; Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Perth, Australia
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Richard L Prince
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia
| | - John T Schousboe
- Park Nicollet Clinic and HealthPartners Institute, HealthPartners, Minneapolis, USA; Division of Health Policy and Management, University of Minnesota, Minneapolis, USA
| | - William D Leslie
- Departments of Medicine and Radiology, University of Manitoba, Winnipeg, Canada
| | - Joshua R Lewis
- Nutrition & Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia; Centre for Kidney Research, Children's Hospital at Westmead School of Public Health, Sydney Medical School, the University of Sydney, Sydney, Australia.
| |
Collapse
|