Skip to main content

Advertisement

SpringerLink
Log in

Machine learning applications to enhance patient specific care for urologic surgery

  • Topic Paper
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Purpose

As computational power has improved over the past 20 years, the daily application of machine learning methods has become more prevalent in daily life. Additionally, there is increasing interest in the clinical application of machine learning techniques. We sought to review the current literature regarding machine learning applications for patient-specific urologic surgical care.

Methods

We performed a broad search of the current literature via the PubMed-Medline and Google Scholar databases up to Dec 2020. The search terms “urologic surgery” as well as “artificial intelligence”, “machine learning”, “neural network”, and “automation” were used.

Results

The focus of machine learning applications for patient counseling is disease-specific. For stone disease, multiple studies focused on the prediction of stone-free rate based on preoperative characteristics of clinical and imaging data. For kidney cancer, many studies focused on advanced imaging analysis to predict renal mass pathology preoperatively. Machine learning applications in prostate cancer could provide for treatment counseling as well as prediction of disease-specific outcomes. Furthermore, for bladder cancer, the reviewed studies focus on staging via imaging, to better counsel patients towards neoadjuvant chemotherapy. Additionally, there have been many efforts on automatically segmenting and matching preoperative imaging with intraoperative anatomy.

Conclusion

Machine learning techniques can be implemented to assist patient-centered surgical care and increase patient engagement within their decision-making processes. As data sets improve and expand, especially with the transition to large-scale EHR usage, these tools will improve in efficacy and be utilized more frequently.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Beckmann JS, Lew D (2016) Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med 8:134. https://doi.org/10.1186/s13073-016-0388-7

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2:719–731. https://doi.org/10.1038/s41551-018-0305-z

    Article  PubMed  Google Scholar 

  3. Tran BX, Vu GT, Ha GH et al (2019) Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med. https://doi.org/10.3390/jcm8030360

    Article  PubMed  PubMed Central  Google Scholar 

  4. Moustris GP, Hiridis SC, Deliparaschos KM, Konstantinidis KM (2011) Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature. Int J Med Robot Comput Assist Surg MRCAS 7:375–392. https://doi.org/10.1002/rcs.408

    Article  CAS  Google Scholar 

  5. Roehrborn CG, Teplitsky S, Das AK (2019) Aquablation of the prostate: a review and update. Can J Urol 26:20–24

    PubMed  Google Scholar 

  6. Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380:1347–1358. https://doi.org/10.1056/NEJMra1814259

    Article  PubMed  Google Scholar 

  7. Kriegeskorte N, Golan T (2019) Neural network models and deep learning. Curr Biol 29:R231–R236. https://doi.org/10.1016/j.cub.2019.02.034

    Article  CAS  PubMed  Google Scholar 

  8. Raman JD, Aditya B, Karim B et al (2010) Residual fragments after percutaneous nephrolithotomy: cost comparison of immediate second look flexible nephroscopy versus expectant management. J Urol 183:188–193. https://doi.org/10.1016/j.juro.2009.08.135

    Article  PubMed  Google Scholar 

  9. Aminsharifi A, Irani D, Pooyesh S et al (2017) Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol 31:461–467. https://doi.org/10.1089/end.2016.0791

    Article  PubMed  Google Scholar 

  10. Aminsharifi A, Irani D, Tayebi S et al (2020) Predicting the postoperative outcome of percutaneous nephrolithotomy with machine learning system: software validation and comparative analysis with Guy’s stone score and the CROES nomogram. J Endourol 34:692–699. https://doi.org/10.1089/end.2019.0475

    Article  PubMed  Google Scholar 

  11. Gomha MA, Sheir KZ, Showky S et al (2004) Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? a neural network or a statistical model? J Urol 172:175–179. https://doi.org/10.1097/01.ju.0000128646.20349.27

    Article  PubMed  Google Scholar 

  12. Seckiner I, Seckiner S, Sen H et al (2017) A neural network—based algorithm for predicting stone-free status after ESWL therapy. Int Braz J Urol Off J Braz Soc Urol 43:1110–1114. https://doi.org/10.1590/S1677-5538.IBJU.2016.0630

    Article  Google Scholar 

  13. Osawa T, Hafez KS, Miller DC et al (2016) Comparison of percutaneous renal mass biopsy and R.E.N.A.L. nephrometry score nomograms for determining benign vs malignant disease and low-risk vs high-risk renal tumors. Urology 96:87–92. https://doi.org/10.1016/j.urology.2016.05.044

    Article  PubMed  Google Scholar 

  14. Kocak B, Kaya OK, Erdim C et al (2020) Artificial intelligence in renal mass characterization: a systematic review of methodologic items related to modeling, performance evaluation, clinical utility, and transparency. AJR Am J Roentgenol 215:1113–1122. https://doi.org/10.2214/AJR.20.22847

    Article  PubMed  Google Scholar 

  15. Tanaka T, Huang Y, Marukawa Y et al (2020) Differentiation of small (≤ 4 cm) renal masses on multiphase contrast-enhanced CT by deep learning. Am J Roentgenol 214:605–612. https://doi.org/10.2214/AJR.19.22074

    Article  Google Scholar 

  16. Yu H, Scalera J, Khalid M et al (2017) Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol 42:2470–2478. https://doi.org/10.1007/s00261-017-1144-1

    Article  Google Scholar 

  17. Auffenberg GB, Ghani KR, Ramani S et al (2019) askMUSIC: leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men. Eur Urol 75:901–907. https://doi.org/10.1016/j.eururo.2018.09.050

    Article  PubMed  Google Scholar 

  18. Zupan B, Demsar J, Kattan MW et al (2000) Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif Intell Med 20:59–75. https://doi.org/10.1016/s0933-3657(00)00053-1

    Article  CAS  PubMed  Google Scholar 

  19. Wong NC, Lam C, Patterson L, Shayegan B (2019) Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy. BJU Int 123:51–57. https://doi.org/10.1111/bju.14477

    Article  PubMed  Google Scholar 

  20. Garapati SS, Hadjiiski L, Cha KH et al (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys 44:5814–5823. https://doi.org/10.1002/mp.12510

    Article  PubMed  Google Scholar 

  21. Liu J, Wang S, Turkbey EB et al (2015) Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features. Med Phys 42:144–153. https://doi.org/10.1118/1.4903056

    Article  PubMed  Google Scholar 

  22. Liu J, Wang S, Linguraru MG et al (2015) Computer-aided detection of exophytic renal lesions on non-contrast CT images. Med Image Anal 19:15–29. https://doi.org/10.1016/j.media.2014.07.005

    Article  PubMed  Google Scholar 

  23. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24:205–219. https://doi.org/10.1016/j.media.2015.06.012

    Article  PubMed  PubMed Central  Google Scholar 

  24. Huo Y, Braxton V, Herrell SD et al (2017) Automated characterization of pyelocalyceal anatomy using CT urograms to aid in management of kidney stones. In: Cardoso MJ, Arbel T, Luo X et al (eds) Computer assisted and robotic endoscopy and clinical image-based procedures. Springer, pp 99–107

    Chapter  Google Scholar 

  25. Wang H, Suh JW, Das SR et al (2013) Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell 35:611–623. https://doi.org/10.1109/TPAMI.2012.143

    Article  PubMed  Google Scholar 

  26. Nosrati MS, Amir-Khalili A, Peyrat J-M et al (2016) Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors. Int J Comput Assist Radiol Surg 11:1409–1418. https://doi.org/10.1007/s11548-015-1331-x

    Article  PubMed  Google Scholar 

  27. Kavoussi NL, Pitt B, Ferguson JM et al (2020) Accuracy of touch-based registration during robotic image-guided partial nephrectomy before and after tumor resection in validated phantoms. J Endourol. https://doi.org/10.1089/end.2020.0363

    Article  PubMed  PubMed Central  Google Scholar 

  28. Svoboda E (2019) Your robot surgeon will see you now. Nature 573:S110–S111. https://doi.org/10.1038/d41586-019-02874-0

    Article  CAS  PubMed  Google Scholar 

  29. Ghani KR, Yunfan L, Hei L et al (2017) Pd46-04 video analysis of skill and technique (vast): machine learning to assess surgeons performing robotic prostatectomy. J Urol 197:e891–e891. https://doi.org/10.1016/j.juro.2017.02.2376

    Article  Google Scholar 

  30. Hung AJ, Chen J, Che Z et al (2018) Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J Endourol 32:438–444. https://doi.org/10.1089/end.2018.0035

    Article  PubMed  Google Scholar 

  31. Hung AJ, Chen J, Ghodoussipour S et al (2019) A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int 124:487–495. https://doi.org/10.1111/bju.14735

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kantarjian H, Yu PP (2015) Artificial intelligence, big data, and cancer. JAMA Oncol 1:573–574. https://doi.org/10.1001/jamaoncol.2015.1203

    Article  PubMed  Google Scholar 

  33. Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20:e262–e273. https://doi.org/10.1016/S1470-2045(19)30149-4

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Urology, Vanderbilt University Medical Center, 3823 The Vanderbilt Clinic, Nashville, Tennessee, 37232, USA

    Patrick W. Doyle & Nicholas L. Kavoussi

Authors
  1. Patrick W. Doyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicholas L. Kavoussi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicholas L. Kavoussi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Doyle, P.W., Kavoussi, N.L. Machine learning applications to enhance patient specific care for urologic surgery. World J Urol 40, 679–686 (2022). https://doi.org/10.1007/s00345-021-03738-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-021-03738-x

Keywords

Search

Navigation