The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection
(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to us...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/10/6/735 |
| _version_ | 1827738575100182528 |
|---|---|
| author | Chun-Hsiang Ouyang Chih-Chi Chen Yu-San Tee Wei-Cheng Lin Ling-Wei Kuo Chien-An Liao Chi-Tung Cheng Chien-Hung Liao |
| author_facet | Chun-Hsiang Ouyang Chih-Chi Chen Yu-San Tee Wei-Cheng Lin Ling-Wei Kuo Chien-An Liao Chi-Tung Cheng Chien-Hung Liao |
| author_sort | Chun-Hsiang Ouyang |
| collection | DOAJ |
| description | (1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers. |
| first_indexed | 2024-03-11T02:46:20Z |
| format | Article |
| id | doaj.art-f24f5adb09834e7faec49d166f781623 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T02:46:20Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-f24f5adb09834e7faec49d166f7816232023-11-18T09:22:01ZengMDPI AGBioengineering2306-53542023-06-0110673510.3390/bioengineering10060735The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture DetectionChun-Hsiang Ouyang0Chih-Chi Chen1Yu-San Tee2Wei-Cheng Lin3Ling-Wei Kuo4Chien-An Liao5Chi-Tung Cheng6Chien-Hung Liao7Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Rehabilitation and Physical Medicine, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Electrical Engineering, Chang Gung University, Taoyuan 33327, TaiwanDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, TaiwanDepartment of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan 33328, Taiwan(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers.https://www.mdpi.com/2306-5354/10/6/735design thinkingartificial intelligencedeep learningtraumahip fracture |
| spellingShingle | Chun-Hsiang Ouyang Chih-Chi Chen Yu-San Tee Wei-Cheng Lin Ling-Wei Kuo Chien-An Liao Chi-Tung Cheng Chien-Hung Liao The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection Bioengineering design thinking artificial intelligence deep learning trauma hip fracture |
| title | The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection |
| title_full | The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection |
| title_fullStr | The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection |
| title_full_unstemmed | The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection |
| title_short | The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection |
| title_sort | application of design thinking in developing a deep learning algorithm for hip fracture detection |
| topic | design thinking artificial intelligence deep learning trauma hip fracture |
| url | https://www.mdpi.com/2306-5354/10/6/735 |
| work_keys_str_mv | AT chunhsiangouyang theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chihchichen theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT yusantee theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT weichenglin theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT lingweikuo theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chienanliao theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chitungcheng theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chienhungliao theapplicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chunhsiangouyang applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chihchichen applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT yusantee applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT weichenglin applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT lingweikuo applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chienanliao applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chitungcheng applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection AT chienhungliao applicationofdesignthinkingindevelopingadeeplearningalgorithmforhipfracturedetection |