Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians.
Journal
Asian journal of andrology
ISSN: 1745-7262
Titre abrégé: Asian J Androl
Pays: China
ID NLM: 100942132
Informations de publication
Date de publication:
09 Jul 2024
09 Jul 2024
Historique:
received:
13
12
2023
accepted:
25
03
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
9
7
2024
Statut:
aheadofprint
Résumé
Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.
Identifiants
pubmed: 38978280
doi: 10.4103/aja202431
pii: 00129336-990000000-00203
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Copyright: ©The Author(s)(2024).
Références
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69S:S36–40.
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 2020;9:14.
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022;23:40–55.
Shaban-Nejad A, Michalowski M, Bianco S. Creative and generative artificial intelligence for personalized medicine and healthcare:hype, reality, or hyperreality?. Exp Biol Med (Maywood) 2023;248:2497–9.
Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril 2020;114:914–20.
Xiong Y, Zhang Y, Zhang F, Wu C, Qin F, et al. Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction:a narrative review. Int J Impot Res 2023;35:95–102.
Ghayda RA, Cannarella R, Calogero AE, Shah R, Rambhatla A, et al. Artificial intelligence in andrology:from semen analysis to image diagnostics. World J Mens Health 2024;42:39–61.
Fernández-López P, Garriga J, Casas I, Yeste M, Bartumeus F. Predicting fertility from sperm motility landscapes. Commun Biol 2022;5:1027.
Finelli R, Leisegang K, Tumallapalli S, Henkel R, Agarwal A. The validity and reliability of computer-aided semen analyzers in performing semen analysis:a systematic review. Transl Androl Urol 2021;10:3069–79.
Dardmeh F, Heidari M, Alipour H. Comparison of commercially available chamber slides for computer-aided analysis of human sperm. Syst Biol Reprod Med 2021;67:168–75.
World Health Organization. WHO Laboratory Manual for the Examination and Processing of Human Semen. 6th ed. Geneva:World Health Organization;2021.
GhoshRoy D, Alvi PA, Santosh KC. Unboxing industry-standard AI models for male fertility prediction with SHAP. Healthcare (Basel) 2023;11:929.
Sahoo AJ, Kumar Y. Seminal quality prediction using data mining methods. Technol Health Care 2014;22:531–45.
Wang Y, Riordon J, Kong T, Xu Y, Nguyen B, et al. Prediction of DNA integrity from morphological parameters using a single-sperm DNA fragmentation index assay. Adv Sci (Weinh) 2019;6:1900712.
Condorelli RA, La Vignera S, Barbagallo F, Alamo A, Mongioì LM, et al. Bio-functional sperm parameters:does age matter?. Front Endocrinol (Lausanne) 2020;11:558374.
Peña FJ, Ortiz Rodriguez JM, Gil MC, Ortega Ferrusola C. Flow cytometry analysis of spermatozoa:is it time for flow spermetry?. Reprod Domest Anim 2018;53. Suppl 2:37–45.
Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, et al. Artificial intelligence in the fertility clinic:status, pitfalls and possibilities. Hum Reprod 2021;36:2429–42.
Vogiatzi P, Pouliakis A, Siristatidis C. An artificial neural network for the prediction of assisted reproduction outcome. J Assist Reprod Genet 2019;36:1441–8.
Cherouveim P, Velmahos C, Bormann CL. Artificial intelligence for sperm selection-a systematic review. Fertil Steril 2023;120:24–31.
Ory J, Tradewell MB, Blankstein U, Lima TF, Nackeeran S, et al. Artificial intelligence based machine learning models predict sperm parameter upgrading after varicocele repair:a multi-institutional analysis. World J Mens Health 2022;40:618–26.
Zeadna A, Khateeb N, Rokach L, Lior Y, Har-Vardi I, et al. Prediction of sperm extraction in nonobstructive azoospermia patients:a machine-learning perspective. Hum Reprod 2020;35:1505–14.
Marginean F, Arvidsson I, Simoulis A, Christian Overgaard N, Åström K, et al. An artificial intelligence-based support tool for automation and standardisation of Gleason grading in prostate biopsies. Eur Urol Focus 2021;7:995–1001.
Chen YF, Lin CS, Hong CF, Lee DJ, Sun C, et al. Design of a clinical decision support system for predicting erectile dysfunction in men using NHIRD dataset. IEEE J Biomed Health Inform 2019;23:2127–37.
Glavaš S, Valenčić L, Trbojević N, Tomašić AM, Turčić N, et al. Erectile function in cardiovascular patients:its significance and a quick assessment using a visual-scale questionnaire. Acta Cardiol 2015;70:712–9.
Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, et al. Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp 2019;3:35.
Winkel DJ, Breit HC, Shi B, Boll DT, Seifert HH, et al. Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning:comparison with PI-RADS v2 assessment scores. Quant Imaging Med Surg 2020;10:808–23.
De Santi B, Spaggiari G, Granata AR, Romeo M, Molinari F, et al. From subjective to objective:a pilot study on testicular radiomics analysis as a measure of gonadal function. Andrology 2022;10:505–17.
Li L, Fan W, Li J, Li Q, Wang J, et al. Abnormal brain structure as a potential biomarker for venous erectile dysfunction:evidence from multimodal MRI and machine learning. Eur Radiol 2018;28:3789–800.
Porpiglia F, Checcucci E, Amparore D, Autorino R, Piana A, et al. Augmented-reality robot-assisted radical prostatectomy using hyper-accuracy three-dimensional reconstruction (HA3D™) technology:a radiological and pathological study. BJU Int 2019;123:834–45.
Darves-Bornoz A, Panken E, Brannigan RE, Halpern JA. Robotic surgery for male infertility. Urol Clin North Am 2021;48:127–35.
Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care 2019;8:2328–31.
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019;28:73–81.
Guo J, Li B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2018;2:174–81.
Cingolani M, Scendoni R, Fedeli P, Cembrani F. Artificial intelligence and digital medicine for integrated home care services in Italy:opportunities and limits. Front Public Health 2023;10:1095001.
Lustgarten Guahmich N, Borini E, Zaninovic N. Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection. Fertil Steril 2023;120:729–34.
Trolice MP, Curchoe C, Quaas AM. Artificial intelligence-the future is now. J Assist Reprod Genet 2021;38:1607–12.
Ma Y, Chen B, Wang H, Hu K, Huang Y. Prediction of sperm retrieval in men with non-obstructive azoospermia using artificial neural networks:leptin is a good assistant diagnostic marker. Hum Reprod 2011;26:294–8.
Akinsal EC, Haznedar B, Baydilli N, Kalinli A, Ozturk A, et al. Artificial neural network for the prediction of chromosomal abnormalities in azoospermic males. Urol J 2018;15:122–5.
Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-García E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine 2019;46:27–9.