An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Jian-Wen Chen; Wan-Ju Lin; Chun-Yuan Lin; Che-Lun Hung; Chen-Pang Hou; Chuan-Yi Tang

doi:10.3390/diagnostics11101767

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Diagnostics (Basel). 2021 Sep 26;11(10):1767. doi: 10.3390/diagnostics11101767.

Authors

Jian-Wen Chen¹, Wan-Ju Lin², Chun-Yuan Lin³, Che-Lun Hung^{4

5}, Chen-Pang Hou^{6

7

8}, Chuan-Yi Tang^{1

9}

Affiliations

¹ Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.
² Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan.
³ Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan.
⁴ Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.
⁵ Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan.
⁶ Department of Urology, Linkou Chang Gung Memorial Hospital, Taoyuan 33302, Taiwan.
⁷ School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan.
⁸ Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan.
⁹ Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan.

Abstract

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.

Keywords: ResUnet model; ranking of bleeding level classification; transurethral resection of the prostate (TURP).