Deep Learning Models for Cystoscopic Recognition of Hunner Lesion in Interstitial Cystitis

Takuya Iwaki; Yoshiyuki Akiyama; Hirokazu Nosato; Manami Kinjo; Aya Niimi; Satoru Taguchi; Yuta Yamada; Yusuke Sato; Taketo Kawai; Daisuke Yamada; Hidenori Sakanashi; Haruki Kume; Yukio Homma; Hiroshi Fukuhara

doi:10.1016/j.euros.2022.12.012

Deep Learning Models for Cystoscopic Recognition of Hunner Lesion in Interstitial Cystitis

Eur Urol Open Sci. 2023 Jan 26:49:44-50. doi: 10.1016/j.euros.2022.12.012. eCollection 2023 Mar.

Authors

Takuya Iwaki^{1

2

3}, Yoshiyuki Akiyama¹, Hirokazu Nosato³, Manami Kinjo⁴, Aya Niimi^{1

5}, Satoru Taguchi¹, Yuta Yamada¹, Yusuke Sato¹, Taketo Kawai⁶, Daisuke Yamada¹, Hidenori Sakanashi³, Haruki Kume¹, Yukio Homma⁷, Hiroshi Fukuhara⁴

Affiliations

¹ Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
² Department of Urology, Center Hospital of the National Center for Global Health and Medicine, Tokyo, Japan.
³ Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
⁴ Department of Urology, Kyorin University School of Medicine, Tokyo, Japan.
⁵ Department of Urology, New Tokyo Hospital, Matsudo, Japan.
⁶ Department of Urology, Teikyo University School of Medicine, Tokyo, Japan.
⁷ Japanese Red Cross Medical Center, Tokyo, Japan.

Abstract

Background: Accurate cystoscopic recognition of Hunner lesions (HLs) is indispensable for better treatment prognosis in managing patients with Hunner-type interstitial cystitis (HIC), but frequently challenging due to its varying appearance.

Objective: To develop a deep learning (DL) system for cystoscopic recognition of a HL using artificial intelligence (AI).

Design setting and participants: A total of 626 cystoscopic images collected from January 8, 2019 to December 24, 2020, consisting of 360 images of HLs from 41 patients with HIC and 266 images of flat reddish mucosal lesions resembling HLs from 41 control patients including those with bladder cancer and other chronic cystitis, were used to create a dataset with an 8:2 ratio of training images and test images for transfer learning and external validation, respectively. AI-based five DL models were constructed, using a pretrained convolutional neural network model that was retrained to output 1 for a HL and 0 for control. A five-fold cross-validation method was applied for internal validation.

Outcome measurements and statistical analysis: True- and false-positive rates were plotted as a receiver operating curve when the threshold changed from 0 to 1. Accuracy, sensitivity, and specificity were evaluated at a threshold of 0.5. Diagnostic performance of the models was compared with that of urologists as a reader study.

Results and limitations: The mean area under the curve of the models reached 0.919, with mean sensitivity of 81.9% and specificity of 85.2% in the test dataset. In the reader study, the mean accuracy, sensitivity, and specificity were, respectively, 83.0%, 80.4%, and 85.6% for the models, and 62.4%, 79.6%, and 45.2% for expert urologists. Limitations include the diagnostic nature of a HL as warranted assertibility.

Conclusions: We constructed the first DL system that recognizes HLs with accuracy exceeding that of humans. This AI-driven system assists physicians with proper cystoscopic recognition of a HL.

Patient summary: In this diagnostic study, we developed a deep learning system for cystoscopic recognition of Hunner lesions in patients with interstitial cystitis. The mean area under the curve of the constructed system reached 0.919 with mean sensitivity of 81.9% and specificity of 85.2%, demonstrating diagnostic accuracy exceeding that of human expert urologists in detecting Hunner lesions. This deep learning system assists physicians with proper diagnosis of a Hunner lesion.

Keywords: Artificial intelligence; Bladder pain syndrome; Deep learning; Hunner lesion; Interstitial cystitis.