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Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis
Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the imag...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8398956/ https://www.ncbi.nlm.nih.gov/pubmed/34441052 http://dx.doi.org/10.3390/medicina57080846 |
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author | Yamamoto, Norio Sukegawa, Shintaro Yamashita, Kazutaka Manabe, Masaki Nakano, Keisuke Takabatake, Kiyofumi Kawai, Hotaka Ozaki, Toshifumi Kawasaki, Keisuke Nagatsuka, Hitoshi Furuki, Yoshihiko Yorifuji, Takashi |
author_facet | Yamamoto, Norio Sukegawa, Shintaro Yamashita, Kazutaka Manabe, Masaki Nakano, Keisuke Takabatake, Kiyofumi Kawai, Hotaka Ozaki, Toshifumi Kawasaki, Keisuke Nagatsuka, Hitoshi Furuki, Yoshihiko Yorifuji, Takashi |
author_sort | Yamamoto, Norio |
collection | PubMed |
description | Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification. |
format | Online Article Text |
id | pubmed-8398956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83989562021-08-29 Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis Yamamoto, Norio Sukegawa, Shintaro Yamashita, Kazutaka Manabe, Masaki Nakano, Keisuke Takabatake, Kiyofumi Kawai, Hotaka Ozaki, Toshifumi Kawasaki, Keisuke Nagatsuka, Hitoshi Furuki, Yoshihiko Yorifuji, Takashi Medicina (Kaunas) Article Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification. MDPI 2021-08-20 /pmc/articles/PMC8398956/ /pubmed/34441052 http://dx.doi.org/10.3390/medicina57080846 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yamamoto, Norio Sukegawa, Shintaro Yamashita, Kazutaka Manabe, Masaki Nakano, Keisuke Takabatake, Kiyofumi Kawai, Hotaka Ozaki, Toshifumi Kawasaki, Keisuke Nagatsuka, Hitoshi Furuki, Yoshihiko Yorifuji, Takashi Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title | Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title_full | Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title_fullStr | Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title_full_unstemmed | Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title_short | Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis |
title_sort | effect of patient clinical variables in osteoporosis classification using hip x-rays in deep learning analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8398956/ https://www.ncbi.nlm.nih.gov/pubmed/34441052 http://dx.doi.org/10.3390/medicina57080846 |
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