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Osteoporosis screening using machine learning and electromagnetic waves

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...

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Autores principales: Albuquerque, Gabriela A., Carvalho, Dionísio D. A., Cruz, Agnaldo S., Santos, João P. Q., Machado, Guilherme M., Gendriz, Ignácio S., Fernandes, Felipe R. S., Barbalho, Ingridy M. P., Santos, Marquiony M., Teixeira, César A. D., Henriques, Jorge M. O., Gil, Paulo, Neto, Adrião D. D., Campos, Antonio L. P. S., Lima, Josivan G., Paiva, Jailton C., Morais, Antonio H. F., Lima, Thaisa Santos, Valentim, Ricardo A. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10409756/
https://www.ncbi.nlm.nih.gov/pubmed/37553424
http://dx.doi.org/10.1038/s41598-023-40104-w
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author Albuquerque, Gabriela A.
Carvalho, Dionísio D. A.
Cruz, Agnaldo S.
Santos, João P. Q.
Machado, Guilherme M.
Gendriz, Ignácio S.
Fernandes, Felipe R. S.
Barbalho, Ingridy M. P.
Santos, Marquiony M.
Teixeira, César A. D.
Henriques, Jorge M. O.
Gil, Paulo
Neto, Adrião D. D.
Campos, Antonio L. P. S.
Lima, Josivan G.
Paiva, Jailton C.
Morais, Antonio H. F.
Lima, Thaisa Santos
Valentim, Ricardo A. M.
author_facet Albuquerque, Gabriela A.
Carvalho, Dionísio D. A.
Cruz, Agnaldo S.
Santos, João P. Q.
Machado, Guilherme M.
Gendriz, Ignácio S.
Fernandes, Felipe R. S.
Barbalho, Ingridy M. P.
Santos, Marquiony M.
Teixeira, César A. D.
Henriques, Jorge M. O.
Gil, Paulo
Neto, Adrião D. D.
Campos, Antonio L. P. S.
Lima, Josivan G.
Paiva, Jailton C.
Morais, Antonio H. F.
Lima, Thaisa Santos
Valentim, Ricardo A. M.
author_sort Albuquerque, Gabriela A.
collection PubMed
description 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.
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spelling pubmed-104097562023-08-10 Osteoporosis screening using machine learning and electromagnetic waves Albuquerque, Gabriela A. Carvalho, Dionísio D. A. Cruz, Agnaldo S. Santos, João P. Q. Machado, Guilherme M. Gendriz, Ignácio S. Fernandes, Felipe R. S. Barbalho, Ingridy M. P. Santos, Marquiony M. Teixeira, César A. D. Henriques, Jorge M. O. Gil, Paulo Neto, Adrião D. D. Campos, Antonio L. P. S. Lima, Josivan G. Paiva, Jailton C. Morais, Antonio H. F. Lima, Thaisa Santos Valentim, Ricardo A. M. Sci Rep Article 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. Nature Publishing Group UK 2023-08-08 /pmc/articles/PMC10409756/ /pubmed/37553424 http://dx.doi.org/10.1038/s41598-023-40104-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Albuquerque, Gabriela A.
Carvalho, Dionísio D. A.
Cruz, Agnaldo S.
Santos, João P. Q.
Machado, Guilherme M.
Gendriz, Ignácio S.
Fernandes, Felipe R. S.
Barbalho, Ingridy M. P.
Santos, Marquiony M.
Teixeira, César A. D.
Henriques, Jorge M. O.
Gil, Paulo
Neto, Adrião D. D.
Campos, Antonio L. P. S.
Lima, Josivan G.
Paiva, Jailton C.
Morais, Antonio H. F.
Lima, Thaisa Santos
Valentim, Ricardo A. M.
Osteoporosis screening using machine learning and electromagnetic waves
title Osteoporosis screening using machine learning and electromagnetic waves
title_full Osteoporosis screening using machine learning and electromagnetic waves
title_fullStr Osteoporosis screening using machine learning and electromagnetic waves
title_full_unstemmed Osteoporosis screening using machine learning and electromagnetic waves
title_short Osteoporosis screening using machine learning and electromagnetic waves
title_sort osteoporosis screening using machine learning and electromagnetic waves
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10409756/
https://www.ncbi.nlm.nih.gov/pubmed/37553424
http://dx.doi.org/10.1038/s41598-023-40104-w
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