Literature DB >> 35027721

Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review.

Yang Xiong1,2, Yangchang Zhang3, Fuxun Zhang1,2, Changjing Wu1, Feng Qin1, Jiuhong Yuan4,5.   

Abstract

Despite the high prevalence of erectile dysfunction, patients are reluctant to seek medical advice, which leads to low diagnostic rates in clinical practice. Artificial intelligence has been widely applied in the diagnosis of many diseases and may alleviate the situation. However, the applications of artificial intelligence in erectile dysfunction have not been reviewed to date. Therefore, the assistance from artificial intelligence needs to be summarized. In this review, 418 publications before January 10, 2021, regarding artificial intelligence applications in diagnosing and predicting erectile dysfunction, were retrieved from five databases, including PubMed, EMBASE, the Cochrane Library, and two Chinese databases (WANFANG and CNKI). In addition, the reference lists of the included studies or relevant reviews were checked to avoid bias. Finally, 30 articles were reviewed to summarize the current status, merits, and limitations of applying artificial intelligence in diagnosing and predicting erectile dysfunction. The results showed that artificial intelligence contributed to developing novel diagnostic questionnaires, equipment, expert systems, classifiers by images and predictive models. However, most of the included studies were not subjected to external validations, resulting in doubt on the generalizability. In the future, more rigorously designed studies with high-quality datasets for erectile dysfunction are required.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Entities:  

Year:  2022        PMID: 35027721     DOI: 10.1038/s41443-022-00528-w

Source DB:  PubMed          Journal:  Int J Impot Res        ISSN: 0955-9930            Impact factor:   2.408


  54 in total

1.  The likely worldwide increase in erectile dysfunction between 1995 and 2025 and some possible policy consequences.

Authors:  I A Ayta; J B McKinlay; R J Krane
Journal:  BJU Int       Date:  1999-07       Impact factor: 5.588

2.  Prevalence and risk factors for erectile dysfunction in the US.

Authors:  Elizabeth Selvin; Arthur L Burnett; Elizabeth A Platz
Journal:  Am J Med       Date:  2007-02       Impact factor: 4.965

3.  Comprehensive Drug Testing of Patient-derived Conditionally Reprogrammed Cells from Castration-resistant Prostate Cancer.

Authors:  Khalid Saeed; Vesa Rahkama; Samuli Eldfors; Dmitry Bychkov; John Patrick Mpindi; Bhagwan Yadav; Lassi Paavolainen; Tero Aittokallio; Caroline Heckman; Krister Wennerberg; Donna M Peehl; Peter Horvath; Tuomas Mirtti; Antti Rannikko; Olli Kallioniemi; Päivi Östling; Taija M Af Hällström
Journal:  Eur Urol       Date:  2016-05-06       Impact factor: 20.096

4.  Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

Authors:  Gustavo A Alonso-Silverio; Fernando Pérez-Escamirosa; Raúl Bruno-Sanchez; José L Ortiz-Simon; Roberto Muñoz-Guerrero; Arturo Minor-Martinez; Antonio Alarcón-Paredes
Journal:  Surg Innov       Date:  2018-05-29       Impact factor: 2.058

5.  LC-MS/MS Software for Screening Unknown Erectile Dysfunction Drugs and Analogues: Artificial Neural Network Classification, Peak-Count Scoring, Simple Similarity Search, and Hybrid Similarity Search Algorithms.

Authors:  Inae Jang; Jae-Ung Lee; Jung-Min Lee; Beom Hee Kim; Bongjin Moon; Jongki Hong; Han Bin Oh
Journal:  Anal Chem       Date:  2019-07-01       Impact factor: 6.986

6.  Altered brain networks in psychogenic erectile dysfunction: a resting-state fMRI study.

Authors:  J Chen; Y Chen; G Chen; Y Dai; Z Yao; Q Lu
Journal:  Andrology       Date:  2017-10-26       Impact factor: 3.842

Review 7.  Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery.

Authors:  Andrew J Hung; Jian Chen; Inderbir S Gill
Journal:  JAMA Surg       Date:  2018-08-01       Impact factor: 14.766

8.  Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

Authors:  Konstantinos Kamnitsas; Christian Ledig; Virginia F J Newcombe; Joanna P Simpson; Andrew D Kane; David K Menon; Daniel Rueckert; Ben Glocker
Journal:  Med Image Anal       Date:  2016-10-29       Impact factor: 8.545

9.  Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes.

Authors:  Jung Hun Oh; Sarah Kerns; Harry Ostrer; Simon N Powell; Barry Rosenstein; Joseph O Deasy
Journal:  Sci Rep       Date:  2017-02-24       Impact factor: 4.379

10.  Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps.

Authors:  Hong Zheng; Jiansong Ji; Liangcai Zhao; Minjiang Chen; An Shi; Linlin Pan; Yiran Huang; Huajie Zhang; Baijun Dong; Hongchang Gao
Journal:  Oncotarget       Date:  2016-09-13
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  1 in total

1.  The Association Between 2, 4-Dichlorophenoxyacetic Acid and Erectile Dysfunction.

Authors:  Wei Wang; Yucheng Ma; Jiawei Chen; Liao Peng; Xiaoshuai Gao; Lede Lin; Fuxun Zhang; Yang Xiong; Feng Qin; Jiuhong Yuan
Journal:  Front Public Health       Date:  2022-06-24
  1 in total

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