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      Is Open Access

      Applications of artificial intelligence in urological setting: a hopeful path to improved care

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.

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          Long Short-Term Memory

          Jürgen Schmidhuber, Jürgen Schmidhuber (2002)
          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            Artificial intelligence in medicine.

            Johanne Tremblay, Pavel Hamet (2017)
            Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
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              A hidden Markov model for predicting transmembrane helices in protein sequences.

              E. Sonnhammer, G von Heijne, A. Krogh (1998)
              A novel method to model and predict the location and orientation of alpha helices in membrane-spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side, loop on the cytoplasmic side, two loops for the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Exerc Rehabil
                Journal ID (iso-abbrev): J Exerc Rehabil
                Title: Journal of Exercise Rehabilitation
                Publisher: Korean Society of Exercise Rehabilitation
                ISSN (Print): 2288-176X
                ISSN (Electronic): 2288-1778
                Publication date Collection: October 2021
                Publication date (Electronic): 26 October 2021
                Volume: 17
                Issue: 5
                Pages: 308-312
                Affiliations
                [1 ]Digital Health Industry Team, National IT Industry Promotion Agency, Jincheon, Korea
                [2 ]Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
                [3 ]Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Sejong, Korea
                Author notes
                [* ]Corresponding author: Khae Hawn Kim, Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, 20 Bodeum 7-ro, Sejong 30099, Korea, Email: kimcho99@ 123456cnuh.co.kr
                Author information
                https://orcid.org/0000-0002-7045-8004
                Article
                Publisher ID: jer-17-5-308
                DOI: 10.12965/jer.2142596.298
                PMC ID: 8566099
                PubMed ID: 34805018
                SO-VID: c4fbaf37-4b22-4c37-9c85-6bb8a56ac0f1
                Copyright © Copyright © 2021 Korean Society of Exercise Rehabilitation
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 12 September 2021
                Date accepted : 10 October 2021
                Categories
                Subject: Review Article

                Keywords: artificial intelligence,machine learning,urology,urological diseases
                Data availability:
                Keywords: artificial intelligence, machine learning, urology, urological diseases

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