An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning
Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/10/1767 |
| _version_ | 1797514893951238144 |
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| author | Jian-Wen Chen Wan-Ju Lin Chun-Yuan Lin Che-Lun Hung Chen-Pang Hou Chuan-Yi Tang |
| author_facet | Jian-Wen Chen Wan-Ju Lin Chun-Yuan Lin Che-Lun Hung Chen-Pang Hou Chuan-Yi Tang |
| author_sort | Jian-Wen Chen |
| collection | DOAJ |
| description | Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists. |
| first_indexed | 2024-03-10T06:37:59Z |
| format | Article |
| id | doaj.art-d3f738436b64429da8160395c8d130ff |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T06:37:59Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-d3f738436b64429da8160395c8d130ff2023-11-22T17:56:26ZengMDPI AGDiagnostics2075-44182021-09-011110176710.3390/diagnostics11101767An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine LearningJian-Wen Chen0Wan-Ju Lin1Chun-Yuan Lin2Che-Lun Hung3Chen-Pang Hou4Chuan-Yi Tang5Department of Computer Science, National Tsing Hua University, Hsinchu 30013, TaiwanDepartment of Mechanical Engineering, National Taiwan University, Taipei 10617, TaiwanDepartment of Computer Science and Information Engineering, Asia University, Taichung 41354, TaiwanInstitute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 11221, TaiwanDepartment of Urology, Linkou Chang Gung Memorial Hospital, Taoyuan 33302, TaiwanDepartment of Computer Science, National Tsing Hua University, Hsinchu 30013, TaiwanBenign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.https://www.mdpi.com/2075-4418/11/10/1767ranking of bleeding level classificationResUnet modeltransurethral resection of the prostate (TURP) |
| spellingShingle | Jian-Wen Chen Wan-Ju Lin Chun-Yuan Lin Che-Lun Hung Chen-Pang Hou Chuan-Yi Tang An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning Diagnostics ranking of bleeding level classification ResUnet model transurethral resection of the prostate (TURP) |
| title | An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning |
| title_full | An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning |
| title_fullStr | An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning |
| title_full_unstemmed | An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning |
| title_short | An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning |
| title_sort | automatic bleeding rank system for transurethral resection of the prostate surgery videos using machine learning |
| topic | ranking of bleeding level classification ResUnet model transurethral resection of the prostate (TURP) |
| url | https://www.mdpi.com/2075-4418/11/10/1767 |
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