REFERENCES
1. Sarker IH. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Comput Sci 2022;3:158.
2. Gumbs AA, Alexander F, Karcz K, et al. White paper: definitions of artificial intelligence and autonomous actions in clinical surgery. Art Int Surg 2022;2:93-100.
3. Brodie A, Dai N, Teoh JY, Decaestecker K, Dasgupta P, Vasdev N. Artificial intelligence in urological oncology: An update and future applications. Urol Oncol 2021;39:379-99.
5. GBD 2019 Benign Prostatic Hyperplasia Collaborators. The global, regional, and national burden of benign prostatic hyperplasia in 204 countries and territories from 2000 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Healthy Longev 2022;3:e754-76.
6. Gratzke C, Bachmann A, Descazeaud A, et al. EAU Guidelines on the assessment of non-neurogenic male lower urinary tract symptoms including benign prostatic obstruction. Eur Urol 2015;67:1099-109.
8. Behcet M, Avcioglu F. Causative Agents of Urinary Tract infections in elderly men with benign prostatic hyperplasia: a microbiological evaluation. Clin Lab 2021;67.
9. Yang CY, Chen GM, Wu YX, et al. Clinical efficacy and complications of transurethral resection of the prostate versus plasmakinetic enucleation of the prostate. Eur J Med Res 2023;28:83.
10. Launer BM, McVary KT, Ricke WA, Lloyd GL. The rising worldwide impact of benign prostatic hyperplasia. BJU Int 2021;127:722-8.
12. Wasserman NF. Benign prostatic hyperplasia: a review and ultrasound classification. Radiol Clin North Am 2006;44:689-710, viii.
13. Stabile A, Giganti F, Kasivisvanathan V, et al. Factors influencing variability in the performance of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer: a systematic literature review. Eur Urol Oncol 2020;3:145-67.
14. Brembilla G, Dell’Oglio P, Stabile A, et al. Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1. Eur Radiol 2020;30:3383-92.
15. Berlin C, Adomeit S, Grover P, et al. Novel AI-based algorithm for the automated computation of coronal parameters in adolescent idiopathic scoliosis patients: a validation study on 100 preoperative full spine X-rays. Global Spine J 2023; doi: 10.1177/21925682231154543.
16. Syer T, Mehta P, Antonelli M, et al. Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies. Cancers 2021;13:3318.
17. Montagne S, Hamzaoui D, Allera A, et al. Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology. Insights Imaging 2021;12:71.
18. Gao W, Zhang P, Wang H, Tuo P, Li Z. Magnetic resonance imaging image feature analysis algorithm under convolutional neural network in the diagnosis and risk stratification of prostate cancer. J Healthc Eng 2021;2021:1034661.
19. Gandhi J, Weissbart SJ, Kim AN, Joshi G, Kaplan SA, Khan SA. Clinical considerations for intravesical prostatic protrusion in the evaluation and management of bladder outlet obstruction secondary to benign prostatic hyperplasia. Curr Urol 2018;12:6-12.
20. Awaisu M, Ahmed M, Lawal AT, et al. Correlation of prostate volume with severity of lower urinary tract symptoms as measured by international prostate symptoms score and maximum urine flow rate among patients with benign prostatic hyperplasia. Afr J Urol 2021;27:16.
21. Wasserman NF, Spilseth B, Golzarian J, Metzger GJ. Use of MRI for lobar classification of benign prostatic hyperplasia: potential phenotypic biomarkers for research on treatment strategies. AJR Am J Roentgenol 2015;205:564-71.
22. Zhang Y, Li W, Zhang Z, et al. Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput 2023;61:757-71.
23. Algohary A, Alhusseini M, Breto AL, et al. Longitudinal changes and predictive value of multiparametric MRI features for prostate cancer patients treated with MRI-guided lattice extreme ablative dose (LEAD) boost radiotherapy. Cancers 2022;14:4475.
24. da Silva LM, Pereira EM, Salles PG, et al. Independent real-world application of a clinical-grade automated prostate cancer detection system. J Pathol 2021;254:147-58.
25. Mao J, Dai Y, Wang L, Pan S, Wang W, Yu H. 'Is it painful'? A qualitative study on experiences of patients before prostate needle biopsy. BMJ Open 2022;12:e056619.
26. Liu YF, Shu X, Qiao XF, et al. Radiomics-based machine learning models for predicting P504s/P63 immunohistochemical expression: a noninvasive diagnostic tool for prostate cancer. Front Oncol 2022;12:911426.
27. Iwamura H, Mizuno K, Akamatsu S, et al. Machine learning diagnosis by immunoglobulin N-glycan signatures for precision diagnosis of urological diseases. Cancer Sci 2022;113:2434-45.
28. Wang Y, Qian H, Shao X, et al. Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer. Spectrochim Acta A Mol Biomol Spectrosc 2023;293:122426.
29. Ezenwa EV, Tijani KH, Jeje EA, et al. The value of percentage free prostate specific antigen (PSA) in the detection of prostate cancer among patients with intermediate levels of total PSA (4.0-10.0 ng/mL) in Nigeria. Arab J Urol 2012;10:394-400.
30. Lenzer J, Brownlee S. US expert panel recommends against prostate cancer screening. BMJ 2011;343:d6479.
31. Pandolfo SD, Del Giudice F, Chung BI, et al. Robotic assisted simple prostatectomy versus other treatment modalities for large benign prostatic hyperplasia: a systematic review and meta-analysis of over 6500 cases. Prostate Cancer Prostatic Dis 2022;1-16.
32. Tzelves L, Feretzakis G, Kalles D, et al. Cluster analysis assessment in proposing a surgical technique for benign prostatic enlargement. Stud Health Technol Inform 2022;295:466-9.
33. Oswald N, Hardman J, Kerr A, et al. Patients want more information after surgery: a prospective audit of satisfaction with perioperative information in lung cancer surgery. J Cardiothorac Surg 2018;13:18.
34. Mourmouris P, Tzelves L, Feretzakis G, et al. The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use? Arch Ital Urol Androl 2021;93:418-24.
35. Lee CL, Kuo HC. Current consensus and controversy on the diagnosis of male lower urinary tract symptoms/benign prostatic hyperplasia. Ci Ji Yi Xue Za Zhi 2017;29:6-11.
36. Armah HB, Parwani AV. Atypical adenomatous hyperplasia (adenosis) of the prostate: a case report with review of the literature. Diagn Pathol 2008;3:34.
37. Trevethan R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health 2017;5:307.
38. Aristidou A, Jena R, Topol EJ. Bridging the chasm between AI and clinical implementation. Lancet 2022;399:620.
39. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689.
40. Hu X, Li B, Mo Y, Alselwi O. Progress in artificial intelligence-based prediction of concrete performance. ACT 2021;19:924-36.
41. Nebot JA. A review of artificial intelligent approaches applied to part accuracy prediction. IJMMM 2010;8:6.
42. Bhattacharya I, Khandwala YS, Vesal S, et al. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022;14:17562872221128791.
43. Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med 2021;27:2176-82.
44. Hong C, Pencina MJ, Wojdyla DM, et al. Predictive Accuracy of stroke risk prediction models across black and white race, sex, and age groups. JAMA 2023;329:306-17.
45. Wang R, Chaudhari P, Davatzikos C. Bias in machine learning models can be significantly mitigated by careful training: evidence from neuroimaging studies. Proc Natl Acad Sci U S A 2023;120:e2211613120.
46. Istasy P, Lee WS, Iansavichene A, et al. The impact of artificial intelligence on health equity in oncology: scoping review. J Med Internet Res 2022;24:e39748.
47. Applicability of Artificial Intelligence in Healthcare in Resource-Poor Settings. Harvard Health Policy Review2022. Available from: https://dimesociety.org/journal/applicability-of-artificial-intelligence-in-healthcare-in-resource-poor-settings/. [Last accessed on 1 Aug 2023].