Shaoxu Wu1,2, Xiong Chen1, Jiexin Pan1, Wen Dong1,2, Xiayao Diao1, Ruiyun Zhang3, Yonghai Zhang4, Yuanfeng Zhang4, Guang Qian5, Hao Chen6, Haotian Lin7,8, Shizhong Xu1, Zhiwen Chen9, Xiaozhou Zhou9, Hongbing Mei10, Chenglong Wu10, Qiang Lv11, Baorui Yuan11, Zeshi Chen1, Wenjian Liao1, Xuefan Yang1, Haige Chen3, Jian Huang1,2, Tianxin Lin1,2,12. 1. Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. 2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China. 3. Department of Urology, Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4. Department of Urology, Shantou Central Hospital, Shantou, China. 5. Peng Cheng Laboratory, Shenzhen, China. 6. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China. 7. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China. 8. Centre for Precision Medicine, Sun Yat-sen University, Guangzhou, China. 9. Departmemt of Urology, The First Hospital Affiliated to Army Medical University, Chongqing, China. 10. Department of Urology, Shenzhen Second People's Hospital, Shenzhen, China. 11. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. 12. State Key Laboratory of Oncology in Southern China, Guangzhou, China.
Abstract
BACKGROUND: Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. METHODS: In total, 69 204 images from 10 729 consecutive patients from 6 hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. RESULTS: The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974 to 0.979) in the internal validation set and 0.990 (95% CI = 0.979 to 0.996), 0.982 (95% CI = 0.974 to 0.988), 0.978 (95% CI = 0.959 to 0.989), and 0.991 (95% CI = 0.987 to 0.994) in different external validation sets. In the CAIDS vs urologists' comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902 to 0.964; sensitivity = 0.954, 95% CI = 0.902 to 0.983) with a short latency of 12 seconds, much more accurate and quicker than the expert urologists. CONCLUSIONS: The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.
BACKGROUND: Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. METHODS: In total, 69 204 images from 10 729 consecutive patients from 6 hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. RESULTS: The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974 to 0.979) in the internal validation set and 0.990 (95% CI = 0.979 to 0.996), 0.982 (95% CI = 0.974 to 0.988), 0.978 (95% CI = 0.959 to 0.989), and 0.991 (95% CI = 0.987 to 0.994) in different external validation sets. In the CAIDS vs urologists' comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902 to 0.964; sensitivity = 0.954, 95% CI = 0.902 to 0.983) with a short latency of 12 seconds, much more accurate and quicker than the expert urologists. CONCLUSIONS: The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.
Authors: Eugene Shkolyar; Xiao Jia; Timothy C Chang; Dharati Trivedi; Kathleen E Mach; Max Q-H Meng; Lei Xing; Joseph C Liao Journal: Eur Urol Date: 2019-09-17 Impact factor: 20.096
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Authors: Maximilian Burger; James W F Catto; Guido Dalbagni; H Barton Grossman; Harry Herr; Pierre Karakiewicz; Wassim Kassouf; Lambertus A Kiemeney; Carlo La Vecchia; Shahrokh Shariat; Yair Lotan Journal: Eur Urol Date: 2012-07-25 Impact factor: 20.096
Authors: Maximilian C Kriegmair; Jan Rother; Bartłomiej Grychtol; Martin Theuring; Manuel Ritter; Cagatay Günes; Maurice S Michel; Nikolaos C Deliolanis; Christian Bolenz Journal: Eur Urol Date: 2019-09-26 Impact factor: 20.096