artificial intelligence

Posts

BJUI journal prizes

July 24, 2020/by Admin

Every year the BJUI awards three prizes to trainee urologists who have played a significant role in contributing to the work published in the journal. The prizes go towards travel costs enabling the trainees to visit international conferences. In 2020, due to the coronavirus pandemic leading to the cancellation of many of these conferences, the usual prize-giving ceremonies have not taken place so here we are introducing you to the prize winners and their work. We hope they will be able to spend their prize money in 2021.

Global prize

This is awarded to authors who are trainees based anywhere in the world other than the Americas and Europe. Usually presented at the USANZ annual meeting. In 2020 the prize was awarded to Sho Uehara for his work on artificial intelligence in prostate cancer diagnosis.

Sho Uehara MD Ph.D Tokyo, Japan
Assistant professor, Department of Urology
Tokyo Medical and Dental University

Email: [email protected]

Sho Uehara received a Ph.D. from the graduate school of Tokyo Medical and Dental University, Tokyo, Japan, in 2018. He is now working as a urologist and an assistant professor at the university hospital. His research interests include prostate cancer diagnostics, and utilization of machine learning for them.

Membership of academic societies:

JUA (The Japanese Urological Association), EAU (European Association of Urology) and AUA (American Urological Association)

Coffey-Krane prize

The Coffey-Krane prize is awarded to an author who is a trainee based in The Americas. Normally presented at the AUA annual conference. Dr Nathan Wong received this year’s award for his work on using machine learning to predict biochemical cancer recurrence following prostatectomy.

Dr Nathan Wong
Associate Professor
Westchester Medical Center and New York Medical College

Dr Nathan Wong is an assistant professor and associate program director in the Department of Urology at Westchester Medical Center and New York Medical College. He specializes in urologic oncology and robotics surgery. His main interests are in technology, clinical trials and surgical education. He completed a Society of Urologic Oncology fellowship at Memorial Sloan Kettering Cancer Center in New York City and urology residency at McMaster University in Hamilton, Ontario in Canada.

John Blandy prize

This prize is for authors who are trainees based in Europe. Presented at the BAUS annual conference; the winner gives a presentation. This year the prize went to Nicholas Raison for his work on a RCT on cognitive training in robotic surgery.

Nicholas Raison is Vattikuti fellow at the MRC Centre for Transplantation and Mucosal Cell Biology, King’s College London and a Urology Specialist Registrar in the London Deanery.

Article of the week: Deep learning computer vision algorithm for detecting kidney stone composition

June 17, 2020/1 Comment/by Khurshid Ghani

Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

If you only have time to read one article this week, we recommend this one.

Deep learning computer vision algorithm for detecting kidney stone composition

Kristian M. Black*, Hei Law^†, Ali Aldoukhi*, Jia Deng^† and Khurshid R. Ghani*

*Department of Urology, University of Michigan, Ann Arbor, MI, and ^†Department of Computer Science, Princeton University, Princeton, NJ, USA

Read the full article

Abstract

Objectives

To assess the recall of a deep learning (DL) method to automatically detect kidney stones composition from digital photographs of stones.

Materials and Methods

A total of 63 human kidney stones of varied compositions were obtained from a stone laboratory including calcium oxalate monohydrate (COM), uric acid (UA), magnesium ammonium phosphate hexahydrate (MAPH/struvite), calcium hydrogen phosphate dihydrate (CHPD/brushite), and cystine stones. At least two images of the stones, both surface and inner core, were captured on a digital camera for all stones. A deep convolutional neural network (CNN), ResNet‐101 (ResNet, Microsoft), was applied as a multi‐class classification model, to each image. This model was assessed using leave‐one‐out cross‐validation with the primary outcome being network prediction recall.

Fig.2. Representative samples for each stone composition prior to cropping. A total of 63 stones were used in this study including: 17 UA, 21 COM, seven struvite, four cystine, and 14 brushite stones consisting of a total of 127 images. Automatic stone composition recall was highest for UA stones at 94%.

Results

The composition prediction recall for each composition was as follows: UA 94% (n = 17), COM 90% (n = 21), MAPH/struvite 86% (n = 7), cystine 75% (n = 4), CHPD/brushite 71% (n = 14). The overall weighted recall of the CNNs composition analysis was 85% for the entire cohort. Specificity and precision for each stone type were as follows: UA (97.83%, 94.12%), COM (97.62%, 95%), struvite (91.84%, 71.43%), cystine (98.31%, 75%), and brushite (96.43%, 75%).

Conclusion

Deep CNNs can be used to identify kidney stone composition from digital photographs with good recall. Future work is needed to see if DL can be used for detecting stone composition during digital endoscopy. This technology may enable integrated endoscopic and laser systems that automatically provide laser settings based on stone composition recognition with the goal to improve surgical efficiency.

Editorial: Can artificial intelligence optimize case selection for hemi‐gland ablation?

February 26, 2020/by Jeremy Teoh

The victory of ‘AlphaGo’ over humans in Go, one of the most complex games with more than 10¹⁷⁰ board configurations, has yielded tremendous attention worldwide [1]. The later version, ‘AlphaGo Zero’, has brought artificial intelligence (AI) to the next level by demonstrating an absolute superiority, winning 100‐0 against the champion‐defeating AlphaGo [2]. It is exciting, and perhaps shocking, to realize what AI can achieve.

In this issue of BJUI, the study by Zhou et al. [3] is the first to utilize AI to optimize case selection for hemi‐gland ablation. In this study, classification and regression tree (CART) analysis, which is a form of supervised machine‐learning algorithm, was used to identify laterality of prostate cancer. In the conventional approach, case selection was based on biopsy results and MRI findings. For the CART model, in addition to the common clinical variables (i.e. age, PSA, prostate volume, biopsy and MRI results), biopsy coordinate‐derived spatial features were also used as model inputs. The model output was the probability of unilateral clinically significant prostate cancer considered suitable for hemi‐gland ablation. Whole‐mount prostatectomy specimens were used as the standard of reference. The CART model correctly identified laterality in 80% of the cases, compared to 73% with the conventional approach. The positive predictive value of the CART model was 83%, compared to 53% with the conventional approach. The superiority of the CART model has been demonstrated, and the area under curve was 0.82.

Artifical intelligence has been widely adopted in the field of Urology [4]. For prostate cancer detection in particular, our group evaluated the diagnostic performances of four machine‐learning models based on clinical variables in a biopsy cohort of 1625 men [5]. The machine‐learning models achieved excellent performances in detecting clinically significant prostate cancer, with an accuracy of up to 95.3%. Algohary et al. [6] constructed three machine‐learning models to identify the presence of clinically significant prostate cancer based on MRI radiomic features in patients who underwent active surveillance. When compared with the Prostate Imaging–Reporting and Data System (PI‐RADS) scoring system, the machine‐learning models were able to improve overall accuracy by 30–80%.

Fehr et al. [7] developed an automated system to classify Gleason scores based on MRI images. The automated system could distinguish between Gleason scores of 6 and 7 or above cancers with an accuracy of up to 93%. The differentiation between Gleason score 3+4 and 4+3 disease also yielded an accuracy of up to 93%. Importantly, the performance of AI and machine‐learning models is highly dependent on the quality and accuracy of the data being input. In terms of prostate cancer detection, either mapping biopsy or whole‐mount prostatectomy specimens should be considered to represent the ‘ground truth’.

There are a number of challenges in implementing AI in clinical practice. First, decision‐making in healthcare requires logical deduction and explanation. The data processing in AI, however, is often described as a ‘black box’. Taking AlphaGo as an example, some ‘moves’ were considered incomprehensible even by world‐class players. Second, although results from AI are promising, there is in general a lack of regulations and standards to assess its safety, efficacy and validity. Liability issues can be problematic in case of medical mishaps. Third, doctors are human. Conflict of interest does exist, and how we can utilize AI in a complementary rather than a competitive manner is a challenging obstacle to overcome.

Nevertheless, AI has huge potential in improving healthcare. Collaborative effort is needed globally to develop and optimize AI systems, and to increase its acceptability and practicality upon implementation. Future studies answering clinically important questions using appropriate standards of reference will be of paramount importance in paving the way for the AI era in urology.

by Jeremy Yuen‐Chun Teoh, Edmund Chiong and Chi‐Fai Ng

References

Silver D, Huang A, Maddison CJ et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529: 484– 9
Silver D, Schrittwieser J, Simonyan K et al. Mastering the game of Go without human knowledge. Nature 2017; 550: 354– 9
Zhou SR, Priester AM, Jayadevan R et al. Using spatial tracking with magnetic resonance imaging/ultrasound‐guided biopsy to identify unilateral prostate cancer. BJU Int 2020; 125: 399– 406
Chen J, Remulla D, Nguyen JH et al. Current status of artificial intelligence applications in urology and their potential to influence clinical practice. BJU Int 2019; 124: 567– 77
Wang G, Teoh JY, Choi KS. Diagnosis of prostate cancer in a Chinese population by using machine learning methods. Conf Proc IEEE Eng Med Biol Soc 2018; 2018: 1– 4
Algohary A, Viswanath S, Shiradkar R et al. Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings. J Magn Reson Imaging 2018; 48: 818– 28
Fehr D, Veeraraghavan H, Wibmer A et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA 2015; 112: E6265– 73

Residents’ podcast: Artificial intelligence applications in urology

October 31, 2019/by Maria Uloko

Maria Uloko is a Urology Resident at the University of Minnesota Hospital. In this podcast she is joined by Dr Christopher Weight, an Associate Professor in the Department of Urology at the University of Minnesota. They are discussing a recent BJUI Article of the month:

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Read the full article

Abstract

Objective

To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.

Materials and methods

A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and nonurologic studies were excluded.