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

International Journal of Impotence Research volume 35, pages 95–102 (2023)Cite this article

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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.

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References

  1. Moor J. The Dartmouth college artificial intelligence conference: the next fifty years. AI Mag. 2006;27:87–91.

    Google Scholar 

  2. Oh JH, Kerns S, Ostrer H, Powell SN, Rosenstein B, Deasy JO. Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes. Sci Rep. 2017;7:43381.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chen J, Chen Y, Chen G, Dai Y, Yao Z, Lu Q. Altered brain networks in psychogenic erectile dysfunction: a resting-state fMRI study. Andrology. 2017;5:1073–81.

    Article  CAS  PubMed  Google Scholar 

  4. Chen YF, Lin CS, Hong CF, Lee DJ, Sun C, Lin HH. Design of a clinical decision support system for predicting erectile dysfunction in men using NHIRD dataset. IEEE J Biomed Health Inf. 2019;23:2127–37.

    Article  Google Scholar 

  5. Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61–78.

    Article  PubMed  Google Scholar 

  6. Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, et al. AIDeveloper: deep learning image classification in life science and beyond. Adv Sci. 2021;8:e2003743.

    Article  Google Scholar 

  7. Zhang CW, Zhang Q, Gao XB, Liu P, Guo HQ. High accuracy and effectiveness with deep neural networks and artificial intelligence in pathological diagnosis of prostate cancer: initial results. J Urol. 2018;199:E935.

    Article  Google Scholar 

  8. Saeed K, Rahkama V, Eldfors S, Bychkov D, Mpindi JP, Yadav B, et al. Comprehensive drug testing of patient-derived conditionally reprogrammed cells from castration-resistant prostate cancer. Eur Urol. 2017;71:319–27.

    Article  PubMed  Google Scholar 

  9. Hung AJ, Chen J, Gill IS. Automated performance metrics and machine learning algorithms to measure surgeon performance and anticipate clinical outcomes in robotic surgery. JAMA Surg. 2018;153:770–1.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zheng H, Ji J, Zhao L, Chen M, Shi A, Pan L, et al. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget. 2016;7:59189–98.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Alonso-Silverio GA, Pérez-Escamirosa F, Bruno-Sanchez R, Ortiz-Simon JL, Muñoz-Guerrero R, Minor-Martinez A, et al. Development of a laparoscopic box trainer based on open source hardware and artificial intelligence for objective assessment of surgical psychomotor skills. Surg Innov. 2018;25:380–8.

    Article  PubMed  Google Scholar 

  12. Li L, Fan W, Li J, Li Q, Wang J, Fan Y, et al. Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning. Eur Radio. 2018;28:3789–800.

    Article  Google Scholar 

  13. NIH Consensus Development Panel on Impotence. NIH consensus conference: impotence. JAMA. 1993;270:83–90.

    Article  Google Scholar 

  14. Selvin E, Burnett AL, Platz EA. Prevalence and risk factors for erectile dysfunction in the US. Am J Med. 2007;120:151–7.

    Article  PubMed  Google Scholar 

  15. Ayta IA, Mckinlay JB, Krane RJ. The likely worldwide increase in erectile dysfunction between 1995 and 2025 and some possible policy consequences. BJU Int. 1999;84:50–6.

    Article  CAS  PubMed  Google Scholar 

  16. Lin H, Zhao L, Wu H, Cao M, Jiang H. Sexual life and medication taking behaviours in young men: an online survey of 92620 respondents in China. Int J Clin Pr. 2020;74:e13417.

    Google Scholar 

  17. Jang I, Lee JU, Lee JM, Kim BH, Moon B, Hong J, et al. 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. Anal Chem. 2019;91:9119–28.

    Article  CAS  PubMed  Google Scholar 

  18. Zhang Y, Tian X, Xia K, Zhuang J, Feng X, Zhou M, et al. Construction of intelligent expert system and its preliminary clinical application in the real world of diagnosis of sexual dysfunction. J Clin Urol. 2018;33:603–6.

    Google Scholar 

  19. Alemozaffar M, Regan MM, Cooperberg MR, Wei JT, Michalski JM, Sandler HM, et al. Prediction of erectile function following treatment for prostate cancer. JAMA. 2011;306:1205–14.

    Article  CAS  PubMed  Google Scholar 

  20. Clark HD, Wells GA, Huët C, Mcalister FA, Salmi LR, Fergusson D, et al. Assessing the quality of randomized trials: Reliability of the jadad scale. Control Clin Trials. 1999;20:448–52.

    Article  CAS  PubMed  Google Scholar 

  21. Wells G, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of case-control studies in meta-analyses. Eur J Epidemiol. 2011;25:603–5.

    Google Scholar 

  22. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. Grade: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336:924–6.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Montorsi F, Oelke M, Henneges C, Brock G, Salonia A, D’anzeo G, et al. Exploratory decision-tree modeling of data from the randomized REACTT trial of tadalafil versus placebo to predict recovery of erectile function after bilateral nerve-sparing radical prostatectomy. Eur Urol. 2016;70:529–37.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A primer for the epidemiologist. Am J Epidemiol. 2019;188:2222–39.

    PubMed  Google Scholar 

  25. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–60.

    Article  CAS  PubMed  Google Scholar 

  26. Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, et al. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol. 2016;18:417–25.

    Article  PubMed  Google Scholar 

  27. Xiong Y, Zhang YC, Zhang FX, Wu CJ, Huang XYZ, Qin F, et al. Risks and subgroups of cognitive impairment under different marital status among older adults: a latent profile analysis. J Mens Health. 2021;17:234–42.

    Google Scholar 

  28. Hossain MA, Saiful Islam SM, Quinn JMW, Huq F, Moni MA. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J Biomed Inf. 2019;100:103313.

    Article  Google Scholar 

  29. Mahmud M, Kaiser MS, Hussain A, Vassanelli S. Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst. 2018;29:2063–79.

    Article  PubMed  Google Scholar 

  30. Binik YM, Servan-Schreiber D, Freiwald S, Hall KS. Intelligent computer-based assessment and psychotherapy. An expert system for sexual dysfunction. J Nerv Ment Dis. 1988;176:387–400.

    Article  CAS  PubMed  Google Scholar 

  31. Selby LV, Narain WR, Russo A, Strong VE, Stetson P. Autonomous detection, grading, and reporting of postoperative complications using natural language processing. Surgery. 2018;164:1300–5.

    Article  PubMed  Google Scholar 

  32. Aramaki E, Miyabe M, Honda C, Isozaki S, Wakamiya S, Sato A, et al. KOTOBAKARI study: using natural language processing of patient short narratives to detect cancer related cognitive impairment. Stud Health Technol Inf. 2019;264:1111–5.

    Google Scholar 

  33. Jannini EA, Granata AM, Hatzimouratidis K, Goldstein I. Use and abuse of Rigiscan in the diagnosis of erectile dysfunction. J Sex Med. 2009;6:1820–9.

    Article  PubMed  Google Scholar 

  34. Van Kollenburg RAA, De Bruin. DM. Validation of the electronic version of the international index of erectile function (IIEF-5 and IIEF-15): a crossover study. J Med Internet Res. 2019;21:e13490.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Glavaš S, Valenčić L, Trbojević N, Tomašić AM, Turčić N, Tibauth S, et al. Erectile function in cardiovascular patients: its significance and a quick assessment using a visual-scale questionnaire. Acta Cardiol. 2015;70:712–9.

    Article  PubMed  Google Scholar 

  36. Rosen RC, Cappelleri JC, Smith MD, Lipsky J, Peña BM. Development and evaluation of an abridged, 5-item version of the international index of erectile function (IIEF-5) as a diagnostic tool for erectile dysfunction. Int J Impot Res. 1999;11:319–26.

    Article  CAS  PubMed  Google Scholar 

  37. Rosen RC, Riley A, Wagner G, Osterloh IH, Kirkpatrick J, Mishra A. The international index of erectile function (IIEF): a multidimensional scale for assessment of erectile dysfunction. Urology. 1997;49:822–30.

    Article  CAS  PubMed  Google Scholar 

  38. Wagner G, Gerstenberg T, Levin RJ. Electrical activity of corpus cavernosum during flaccidity and erection of the human penis: a new diagnostic method? J Urol. 1989;142:723–5.

    Article  CAS  PubMed  Google Scholar 

  39. Krafft ME, Boñaga L. Computergestützte Auswertung des glattmuskulären Elektromyogramms der Corpora cavernosa (CC-EMG) mittels Fast-Fourier-Transformation (FFT). Aktuelle Urologie. 1996;27:291–8.

    Article  Google Scholar 

  40. Stief CG, Kellner B, Hartung C, Hauck E, Schlote N, Truss M, et al. Computer-assisted evaluation of the smooth-muscle electromyogram of the corpora cavernosa by fast Fourier transformation. Eur Urol. 1997;31:329–34.

    Article  CAS  PubMed  Google Scholar 

  41. Gorek M, Stief CG, Hartung C, Jonas U. Computer-assisted interpretation of electromyograms of corpora cavernosa using fuzzy logic. World J Urol. 1997;15:65–70.

    Article  CAS  PubMed  Google Scholar 

  42. Kellner B, Stief CG, Hinrichs H, Hartung C. Computerized classification of corpus cavernosum electromyogram signals by the use of discriminant analysis and artificial neural networks to support diagnosis of erectile dysfunction. Urol Res. 2000;28:6–13.

    Article  CAS  PubMed  Google Scholar 

  43. El-Sakka AI. Association between International Index of Erectile Function and axial penile rigidity in patients with erectile dysfunction. Int J Impot Res. 2003;15:426–9.

    Article  CAS  PubMed  Google Scholar 

  44. Ng WK, Ng EY, Chia SJ. The engineering analysis of bioheat equation and penile hemodynamic relationships in the diagnosis of erectile dysfunction: part I-theoretical study and mathematical modeling. Int J Impot Res. 2008;20:295–306.

    Article  CAS  PubMed  Google Scholar 

  45. Ng WK, Ng YK, Tan YK. Qualitative study of sexual functioning in couples with erectile dysfunction: prospective evaluation of the thermography diagnostic system. J Reprod Med. 2009;54:698–705.

    PubMed  Google Scholar 

  46. Ng EY, Ng WK, Huang J, Tan YK. The engineering analysis of bioheat equation and penile hemodynamic relationships in the diagnosis of erectile dysfunction: part II-model optimization using the ANOVA and Taguchi method. Int J Impot Res. 2008;20:285–94.

    Article  CAS  PubMed  Google Scholar 

  47. Udelson D, Nehra A, Hatzichristou DG, Azadzoi K, Moreland RB, Krane J, et al. Engineering analysis of penile hemodynamic and structural-dynamic relationships: Part I–Clinical implications of penile tissue mechanical properties. Int J Impot Res. 1998;10:15–24.

    Article  CAS  PubMed  Google Scholar 

  48. Udelson D, Nehra A, Hatzichristou DG, Azadzoi K, Moreland RB, Krane RJ, et al. Engineering analysis of penile hemodynamic and structural-dynamic relationships: part III–Clinical considerations of penile hemodynamic and rigidity erectile responses. Int J Impot Res. 1998;10:89–99.

    Article  CAS  PubMed  Google Scholar 

  49. Udelson D, Park K, Sadeghi-Nejad H, Salimpour P, Krane RJ, Goldstein I. Axial penile buckling forces vs Rigiscan radial rigidity as a function of intracavernosal pressure: why Rigiscan does not predict functional erections in individual patients. Int J Impot Res. 1999;11:327–37.

    Article  CAS  PubMed  Google Scholar 

  50. Yuan J, Qin F. Intelligent monitor of erectile function: US, 9888878. 2018.2.13. www.freepatentsonline.com/9888878.html

  51. Cera N, Di Pierro ED, Ferretti A, Tartaro A, Romani GL, Perrucci MG. Brain networks during free viewing of complex erotic movie: new insights on psychogenic erectile dysfunction. PLoS ONE. 2014;9:e105336.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Elhanbly S, Elkholy A. Nocturnal penile erections: the role of RigiScan in the diagnosis of vascular erectile dysfunction. J Sex Med. 2012;9:3219–26.

    Article  PubMed  Google Scholar 

  53. Nakahara T, Narula J, Tijssen JGP, Agarwal S, Chowdhury MM, Coughlin PA. et al. (18)F-Fluoride positron emission tomographic imaging of penile arteries and erectile dysfunction. J Am Coll Cardiol. 2019;73:1386–94.

    Article  PubMed  Google Scholar 

  54. Haskins AE, Han PK, Lucas FL, Bristol I, Hansen M. Development of clinical models for predicting erectile function after localized prostate cancer treatment. Int J Urol. 2014;21:1227–33.

    Article  PubMed  Google Scholar 

  55. Rabbani F, Stapleton AM, Kattan MW, Wheeler TM, Scardino PT. Factors predicting recovery of erections after radical prostatectomy. J Urol. 2000;164:1929–34.

    Article  CAS  PubMed  Google Scholar 

  56. Kwon T, Lee C, Jung J, Kim CS. Neurovascular bundle size measured on 3.0-T magnetic resonance imaging is associated with the recovery of erectile function after robot-assisted radical prostatectomy. Urol Oncol. 2017;35:542.e511–42.e517.

    Article  Google Scholar 

  57. Safavy S, Kilday PS, Slezak JM, Abdelsayed GA, Harrison TN, Jacobsen SJ, et al. Effect of a smoking cessation program on sexual function recovery following robotic prostatectomy at Kaiser Permanente Southern California. Perm J. 2017;21:16–138.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Hamidi N, Altinbas NK, Gokce MI, Suer E, Yagci C, Baltaci S, et al. Preliminary results of a new tool to evaluate cavernous body fibrosis following radical prostatectomy: penile elastography. Andrology. 2017;5:999–1006.

    Article  CAS  PubMed  Google Scholar 

  59. KleinJan GH, Sikorska K, Korne CM, Brouwer OR, Buckle T, Tillier C, et al. A prediction model relating the extent of intraoperative fascia preservation to erectile dysfunction after nerve-sparing robot-assisted radical prostatectomy. J Robot Surg. 2019;13:455–62.

    Article  CAS  PubMed  Google Scholar 

  60. Abdollah F, Sun M, Suardi N, Gallina A, Bianchi M, Tutolo M, et al. Prediction of functional outcomes after nerve-sparing radical prostatectomy: results of conditional survival analyses. Eur Urol. 2012;62:42–52.

    Article  PubMed  Google Scholar 

  61. Briganti A, Gallina A, Suardi N, Capitanio U, Tutolo M, Bianchi M, et al. Predicting erectile function recovery after bilateral nerve sparing radical prostatectomy: a proposal of a novel preoperative risk stratification. J Sex Med. 2010;7:2521–31.

    Article  PubMed  Google Scholar 

  62. Novara G, Ficarra V, D’Elia C, Secco S, De Gobbi A, Cavalleri S, et al. Preoperative criteria to select patients for bilateral nerve-sparing robotic-assisted radical prostatectomy. J Sex Med. 2010;7:839–45.

    Article  PubMed  Google Scholar 

  63. Cozzi G, Musi G, Monturano M, Bagnardi V, Frassoni S, Jereczek-Fossa BA, et al. Sexual function recovery after robot-assisted radical prostatectomy: outcomes from an Italian referral centre and predicting nomogram. Andrologia. 2019;51:e13385.

    Article  PubMed  Google Scholar 

  64. Mulhall JP, Kattan MW, Bennett NE, Stasi J, Nascimento B, Eastham J, et al. Development of nomograms to predict the recovery of erectile function following radical prostatectomy. J Sex Med. 2019;16:1796–802.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Huynh LM, Skarecky D, Wilson T, Lau C, Wagner C, Porter J, et al. Internal and external validation of a 90-day percentage erection fullness score model predicting potency recovery following robot-assisted radical prostatectomy. Eur Urol Oncol. 2020;3:657–62.

    Article  PubMed  Google Scholar 

  66. Huynh LM, Osann K, Skarecky D, Ahlering TE. Predictive modelling of 2-year potency outcomes using a novel 90-day erection fullness scale after robot-assisted radical prostatectomy. BJU Int. 2018;122:249–54.

    Article  PubMed  Google Scholar 

  67. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. 2014;507:315–22.

    Article  Google Scholar 

  68. Schymura MJ, Sun L, Percy-Laurry A. Prostate cancer collaborative stage data items–their definitions, quality, usage, and clinical implications: a review of SEER data for 2004-10. Cancer. 2014;120:3758–70.

    Article  CAS  PubMed  Google Scholar 

  69. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009;37:D885–90.

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the Natural Science Foundation of China [grant numbers 81871147 and 81671453]; and the funding of Health Commission of Sichuan Province [grant numbers 20PJ184 and 20PJ063].

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Authors and Affiliations

  1. Andrology Laboratory, West China Hospital, Sichuan University, Chengdu, China

    Yang Xiong, Fuxun Zhang, Changjing Wu, Feng Qin & Jiuhong Yuan

  2. Department of Urology, West China Hospital, Sichuan University, Chengdu, China

    Yang Xiong, Fuxun Zhang & Jiuhong Yuan

  3. Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, China

    Yangchang Zhang

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  1. Yang Xiong
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  5. Feng Qin
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Contributions

Conception and design: YX, FQ, and JY. Administrative support: FQ and JY. Provision of study materials or patients: YX and FQ. Collection and assembly of data: YX, FQ, and YZ. Data analysis and interpretation: YX, YZ, FZ, and CW. Manuscript writing: all authors. Final approval of manuscript: all authors.

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Correspondence to Jiuhong Yuan.

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Xiong, Y., Zhang, Y., Zhang, F. et al. Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review. Int J Impot Res 35, 95–102 (2023). https://doi.org/10.1038/s41443-022-00528-w

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