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Cho Y, Park JM, Youn S. General Overview of Artificial Intelligence for Interstitial Cystitis in Urology. Int Neurourol J 2023; 27:S64-72. [PMID: 38048820 DOI: 10.5213/inj.2346294.147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023] Open
Abstract
Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.
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Affiliation(s)
- Yongwon Cho
- Department of AI Center, Korea University Anam Hospital, Seoul, Korea
| | - Jong Mok Park
- Department of Urology, Chungnam National University Sejong Hospital, Chungnam National University College of Medicine, Sejong, Korea
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2
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Reis LO. ChatGPT for medical applications and urological science. Int Braz J Urol 2023; 49:652-656. [PMID: 37338818 PMCID: PMC10482461 DOI: 10.1590/s1677-5538.ibju.2023.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 04/30/2023] [Indexed: 06/21/2023] Open
Affiliation(s)
- Leonardo O. Reis
- Universidade Estadual de CampinasFaculdade de Ciências MédicasDepartamento de UrologiaSão PauloCampinasBrasilUroScience e Departamento de Urologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas - UNICAMP, Campinas, São Paulo, Brasil
- Pontifícia Universidade Católica de CampinasFaculdade de Ciências da VidaDepartamento de ImunoncologiaSão PauloCampinasBrasilDepartamento de Imunoncologia, Faculdade de Ciências da Vida, Pontifícia Universidade Católica de Campinas, PUC-Campinas, Campinas, São Paulo, Brasil
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Yoo KS. Motion prediction using brain waves based on artificial intelligence deep learning recurrent neural network. J Exerc Rehabil 2023; 19:219-227. [PMID: 37662525 PMCID: PMC10468292 DOI: 10.12965/jer.2346242.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023] Open
Abstract
Electroencephalogram (EEG) research has gained widespread use in various research domains due to its valuable insights into human body movements. In this study, we investigated the optimization of motion discrimination prediction by employing an artificial intelligence deep learning recurrent neural network (gated recurrent unit, GRU) on unique EEG data generated from specific movement types among EEG signals. The experiment involved participants categorized into five difficulty levels of postural control, targeting gymnasts in their twenties and college students majoring in physical education (n=10). Machine learning techniques were applied to extract brain-motor patterns from the collected EEG data, which consisted of 32 channels. The EEG data underwent spectrum analysis using fast Fourier transform conversion, and the GRU model network was utilized for machine learning on each EEG frequency domain, thereby improving the performance index of the learning operation process. Through the development of the GRU network algorithm, the performance index achieved up to a 15.92% improvement compared to the accuracy of existing models, resulting in motion recognition accuracy ranging from a minimum of 94.67% to a maximum of 99.15% between actual and predicted values. These optimization outcomes are attributed to the enhanced accuracy and cost function of the GRU network algorithm's hidden layers. By implementing motion identification optimization based on artificial intelligence machine learning results from EEG signals, this study contributes to the emerging field of exercise rehabilitation, presenting an innovative paradigm that reveals the interconnectedness between the brain and the science of exercise.
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Affiliation(s)
- Kyoung-Seok Yoo
- Department of Sport Sciences, Hannam University, Daejeon,
Korea
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4
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Ferro M, Falagario UG, Barone B, Maggi M, Crocetto F, Busetto GM, Giudice FD, Terracciano D, Lucarelli G, Lasorsa F, Catellani M, Brescia A, Mistretta FA, Luzzago S, Piccinelli ML, Vartolomei MD, Jereczek-Fossa BA, Musi G, Montanari E, Cobelli OD, Tataru OS. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics (Basel) 2023; 13:2308. [PMID: 37443700 DOI: 10.3390/diagnostics13132308] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Ugo Giovanni Falagario
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Biagio Barone
- Urology Unit, Department of Surgical Sciences, AORN Sant'Anna e San Sebastiano, 81100 Caserta, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Michele Catellani
- Department of Urology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Antonio Brescia
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Stefano Luzzago
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mattia Luca Piccinelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | | | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Division of Radiation Oncology, IEO-European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mures, 540142 Târgu Mures, Romania
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Cho Y, Youn S. Intravesical Bladder Treatment and Deep Learning Applications to Improve Irritative Voiding Symptoms Caused by Interstitial Cystitis: A Literature Review. Int Neurourol J 2023; 27:S13-20. [PMID: 37280755 DOI: 10.5213/inj.2346106.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 06/08/2023] Open
Abstract
Our comprehension of interstitial cystitis/painful bladder syndrome (IC/PBS) has evolved over time. The term painful bladder syndrome, preferred by the International Continence Society, is characterized as "a syndrome marked by suprapubic pain during bladder filling, alongside increased daytime and nighttime frequency, in the absence of any proven urinary infection or other pathology." The diagnosis of IC/PBS primarily relies on symptoms of urgency/frequency and bladder/pelvic pain. The exact pathogenesis of IC/PBS remains a mystery, but it is postulated to be multifactorial. Theories range from bladder urothelial abnormalities, mast cell degranulation in the bladder, bladder inflammation, to altered bladder innervation. Therapeutic strategies encompass patient education, dietary and lifestyle modifications, medication, intravesical therapy, and surgical intervention. This article delves into the diagnosis, treatment, and prognosis prediction of IC/PBS, presenting the latest research findings, artificial intelligence technology applications in diagnosing major diseases in IC/PBS, and emerging treatment alternatives.
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Affiliation(s)
- Yongwon Cho
- AI Center, Korea University Anam Hospital, Seoul, Korea
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Baray SB, Abdelmoniem M, Mahmud S, Kabir S, Faisal MAA, Chowdhury MEH, Abbas TO. Automated measurement of penile curvature using deep learning-based novel quantification method. Front Pediatr 2023; 11:1149318. [PMID: 37138577 PMCID: PMC10150132 DOI: 10.3389/fped.2023.1149318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 03/13/2023] [Indexed: 05/05/2023] Open
Abstract
Objective Develop a reliable, automated deep learning-based method for accurate measurement of penile curvature (PC) using 2-dimensional images. Materials and methods A set of nine 3D-printed models was used to generate a batch of 913 images of penile curvature (PC) with varying configurations (curvature range 18° to 86°). The penile region was initially localized and cropped using a YOLOv5 model, after which the shaft area was extracted using a UNet-based segmentation model. The penile shaft was then divided into three distinct predefined regions: the distal zone, curvature zone, and proximal zone. To measure PC, we identified four distinct locations on the shaft that reflected the mid-axes of proximal and distal segments, then trained an HRNet model to predict these landmarks and calculate curvature angle in both the 3D-printed models and masked segmented images derived from these. Finally, the optimized HRNet model was applied to quantify PC in medical images of real human patients and the accuracy of this novel method was determined. Results We obtained a mean absolute error (MAE) of angle measurement <5° for both penile model images and their derivative masks. For real patient images, AI prediction varied between 1.7° (for cases of ∼30° PC) and approximately 6° (for cases of 70° PC) compared with assessment by a clinical expert. Discussion This study demonstrates a novel approach to the automated, accurate measurement of PC that could significantly improve patient assessment by surgeons and hypospadiology researchers. This method may overcome current limitations encountered when applying conventional methods of measuring arc-type PC.
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Affiliation(s)
- Sriman Bidhan Baray
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Mohamed Abdelmoniem
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Saidul Kabir
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh
| | | | | | - Tariq O. Abbas
- Department of Surgery, Weill Cornell Medicine-Qatar, Ar-Rayyan, Qatar
- Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar
- College of Medicine, Qatar University, Doha, Qatar
- Correspondence: Tariq O. Abbas
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Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P, Reuter VE, True LD. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform 2023; 14:100177. [PMID: 36654741 PMCID: PMC9841212 DOI: 10.1016/j.jpi.2022.100177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 12/20/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
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Affiliation(s)
- Anil V. Parwani
- The Ohio State University, Columbus, Ohio, USA
- Corresponding author.
| | - Ankush Patel
- The Ohio State University, 2441 60th Ave SE, Mercer Island, Washington 98040, USA
| | - Ming Zhou
- Tufts University, Medford, Massachusetts, USA
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Abbas TO, AbdelMoniem M, Chowdhury MEH. Automated quantification of penile curvature using artificial intelligence. Front Artif Intell 2022; 5:954497. [PMID: 36111321 PMCID: PMC9468331 DOI: 10.3389/frai.2022.954497] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTo develop and validate an artificial intelligence (AI)-based algorithm for capturing automated measurements of Penile curvature (PC) based on 2-dimensional images.Materials and methodsNine 3D-printed penile models with differing curvature angles (ranging from 18 to 88°) were used to compile a 900-image dataset featuring multiple camera positions, inclination angles, and background/lighting conditions. The proposed framework of PC angle estimation consisted of three stages: automatic penile area localization, shaft segmentation, and curvature angle estimation. The penile model images were captured using a smartphone camera and used to train and test a Yolov5 model that automatically cropped the penile area from each image. Next, an Unet-based segmentation model was trained, validated, and tested to segment the penile shaft, before a custom Hough-Transform-based angle estimation technique was used to evaluate degree of PC.ResultsThe proposed framework displayed robust performance in cropping the penile area [mean average precision (mAP) 99.4%] and segmenting the shaft [Dice Similarity Coefficient (DSC) 98.4%]. Curvature angle estimation technique generally demonstrated excellent performance, with a mean absolute error (MAE) of just 8.5 when compared with ground truth curvature angles.ConclusionsConsidering current intra- and inter-surgeon variability of PC assessments, the framework reported here could significantly improve precision of PC measurements by surgeons and hypospadiology researchers.
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Affiliation(s)
- Tariq O. Abbas
- Weill Cornell Medicine-Qatar, Ar-Rayyan, Qatar
- Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar
- College of Medicine, Qatar University, Doha, Qatar
- *Correspondence: Tariq O. Abbas
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Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.
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