Osteoporosis screening using machine learning and electromagnetic waves.

Autor: Albuquerque GA; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil. gabriela.albuquerque@navi.ifrn.edu.br.; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil. gabriela.albuquerque@navi.ifrn.edu.br., Carvalho DDA; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil.; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil., Cruz AS; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil.; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil., Santos JPQ; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil., Machado GM; LyRIDS, ECE-Engineering School, Paris, France., Gendriz IS; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil., Fernandes FRS; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil., Barbalho IMP; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil., Santos MM; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil., Teixeira CAD; Department of Informatics Engineering, Univ. Coimbra, Centre for Informatics and Systems of the University of Coimbra (CISUC), Coimbra, Portugal., Henriques JMO; Department of Informatics Engineering, Univ. Coimbra, Centre for Informatics and Systems of the University of Coimbra (CISUC), Coimbra, Portugal., Gil P; Department of Electrical and Computer Engineering, School of Science and Technology, New University of Lisbon, Lisbon, Portugal., Neto ADD; Post-Graduation Program on Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil., Campos ALPS; Post-Graduation Program on Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil., Lima JG; University Hospital Onofre Lopes, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil., Paiva JC; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil., Morais AHF; Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil., Lima TS; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil.; Ministry of Health, Brasília, Brazil., Valentim RAM; Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Aug 08; Vol. 13 (1), pp. 12865. Date of Electronic Publication: 2023 Aug 08.
DOI: 10.1038/s41598-023-40104-w
Abstrakt: Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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