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請使用永久網址來引用或連結此文件:
http://libir.tmu.edu.tw/handle/987654321/61507
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| 題名: | The Deep Learning Model of Osteoporosis Screening by Combining Use of EHR and Chest X-ray Images |
| 作者: | DEWI, SARI RAHMAWATI KUSUMA |
| 貢獻者: | 醫學資訊研究所碩士班
林明錦 |
| 關鍵詞: | deep learning;osteoporosis;screening;chest x-ray;EHR |
| 日期: | 2021-08-19 |
| 上傳時間: | 2022-03-28 19:08:52 (UTC+8) |
| 摘要: | Title of Thesis : The Deep Learning Model of Osteoporosis Screening
by Combining Use of EHR and Chest X-ray Images
Author : Sari Rahmawati Kusuma Dewi
Thesis advised by : Associate. Professor. Ming-Chin Lin, MD, PhD
Background: Osteoporosis is the one of common disease in the elderly worldwide. The
incidence of osteoporosis was increased hand in hand with increasing the elderly population at
some country even though the prevention action and clinical treatment had been done. It is
caused by asymptomatic osteoporosis only can be diagnosed until the symptoms happen.
Therefore, we need the approach for detecting osteoporosis as early as possible for
minimalizing the burden of osteoporosis.
Research Aims: The aim of this study is to develop the osteoporosis screening model for
supporting osteoporosis diagnosis and preventive action.
Material and Methods: We were collected patient data from Shuang Ho Hospital, consist of
image data and tabular data. Osteoporosis diagnosis was established using T-score refer to
WHO criteria. The deep learning (DL) model have been developed through data pre-processing,
generating deep learning models, and evaluating the performance models. In this study, the
fusion deep learning model had been compared with image-only and electronic health record
(EHR)-only model. The DL model predictive performance had been evaluated using accuracy,
sensitivity (recall), precision, F-1 score, and area under ROC curve (AUC).
Results: The 1148 patient data were collected from Shuang-Ho Hospital Information System,
including chest x-ray images and patient meta data. The result of this study showed that the
xi
fusion model highest average predictive performance, including 0.80 of accuracy, 0.80 of recall,
0.81 of precision, 0.80 of F-1 score, and 0.80 of AUC.
Conclusions: The performance of fusion model is higher than image-only and EHR-only
model. Our proposed model can be alternative for osteoporosis screening. The further research
is ongoing process that is aimed to improve the deep learning performance. |
| 描述: | 碩士
指導教授：林明錦
委員：邱泓文
委員：蘇家玉 |
| 資料類型: | thesis |
| 顯示於類別: | [醫學資訊研究所] 博碩士論文
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