Applications of artificial intelligence in benign prostatic hyperplasia

Saakshi Bansal , Yanjinlkham Chuluunbaatar , Andrew Brodie , Nikhil Vasdev

Artificial Intelligence Surgery ›› 2023, Vol. 3 ›› Issue (2) : 129 -39.

PDF
Artificial Intelligence Surgery ›› 2023, Vol. 3 ›› Issue (2) :129 -39. DOI: 10.20517/ais.2023.07
Review

Applications of artificial intelligence in benign prostatic hyperplasia

Author information +
History +
PDF

Abstract

The advancement of computational abilities has taken us from the days of machines performing simple, one-dimensional tasks to themselves learning and applying knowns to unknowns. Artificial intelligence (AI) has become integral in daily life, yet there is vast room for application in surgery. Cancer research can divert attention from more prevalent benign diseases which may equally cause a significant impact on quality of life. Here we review recent advancements in the field of AI for diagnostics, management, and prognostication of benign prostatic hyperplasia, evaluating the strengths and limitations of these approaches with implications for future research.

Keywords

Artificial intelligence / benign prostate / urology / deep learning

Cite this article

Download citation ▾
Saakshi Bansal, Yanjinlkham Chuluunbaatar, Andrew Brodie, Nikhil Vasdev. Applications of artificial intelligence in benign prostatic hyperplasia. Artificial Intelligence Surgery, 2023, 3(2): 129-39 DOI:10.20517/ais.2023.07

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Sarker IH.AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems.SN Comput Sci2022;3:158 PMCID:PMC8830986

[2]

Gumbs AA,Karcz K.White paper: definitions of artificial intelligence and autonomous actions in clinical surgery.Art Int Surg2022;2:93-100

[3]

Brodie A,Teoh JY,Dasgupta P.Artificial intelligence in urological oncology: An update and future applications.Urol Oncol2021;39:379-99

[4]

Rawla P.Epidemiology of prostate cancer.World J Oncol2019;10:63-89 PMCID:PMC6497009

[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 Longev2022;3:e754-76 PMCID:PMC9640930

[6]

Gratzke C,Descazeaud A.EAU Guidelines on the assessment of non-neurogenic male lower urinary tract symptoms including benign prostatic obstruction.Eur Urol2015;67:1099-109

[7]

Emberton M.Acute urinary retention in men: an age old problem.BMJ1999;318:921-5 PMCID:PMC1115341

[8]

Behcet M.Causative Agents of Urinary Tract infections in elderly men with benign prostatic hyperplasia: a microbiological evaluation.Clin Lab2021;67:

[9]

Yang CY,Wu YX.Clinical efficacy and complications of transurethral resection of the prostate versus plasmakinetic enucleation of the prostate.Eur J Med Res2023;28:83 PMCID:PMC9938974

[10]

Launer BM,Ricke WA.The rising worldwide impact of benign prostatic hyperplasia.BJU Int2021;127:722-8 PMCID:PMC8170717

[11]

Thorpe A.Benign prostatic hyperplasia.Lancet2003;361:1359-67

[12]

Wasserman NF.Benign prostatic hyperplasia: a review and ultrasound classification.Radiol Clin North Am2006;44:689-710, viii

[13]

Stabile A,Kasivisvanathan V.Factors influencing variability in the performance of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer: a systematic literature review.Eur Urol Oncol2020;3:145-67 PMCID:PMC8942295

[14]

Brembilla G,Stabile A.Interreader variability in prostate MRI reporting using Prostate Imaging Reporting and Data System version 2.1.Eur Radiol2020;30:3383-92

[15]

Berlin C,Grover P.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 J2023;:

[16]

Syer T,Antonelli M.Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies.Cancers2021;13:3318. PMCID:PMC8268820

[17]

Montagne S,Allera A.Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.Insights Imaging2021;12:71 PMCID:PMC8179870

[18]

Gao W,Wang H,Li Z.Magnetic resonance imaging image feature analysis algorithm under convolutional neural network in the diagnosis and risk stratification of prostate cancer.J Healthc Eng2021;2021:1034661 PMCID:PMC8643240

[19]

Gandhi J,Kim AN,Kaplan SA.Clinical considerations for intravesical prostatic protrusion in the evaluation and management of bladder outlet obstruction secondary to benign prostatic hyperplasia.Curr Urol2018;12:6-12 PMCID:PMC6198776

[20]

Awaisu M,Lawal AT.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 Urol2021;27:16

[21]

Wasserman NF,Golzarian J.Use of MRI for lobar classification of benign prostatic hyperplasia: potential phenotypic biomarkers for research on treatment strategies.AJR Am J Roentgenol2015;205:564-71 PMCID:PMC4816487

[22]

Zhang Y,Zhang Z.Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.Med Biol Eng Comput2023;61:757-71

[23]

Algohary A,Breto 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.Cancers2022;14:4475 PMCID:PMC9496901

[24]

da Silva LM,Salles PG.Independent real-world application of a clinical-grade automated prostate cancer detection system.J Pathol2021;254:147-58 PMCID:PMC8252036

[25]

Mao J,Wang L,Wang W.'Is it painful'? A qualitative study on experiences of patients before prostate needle biopsy.BMJ Open2022;12:e056619 PMCID:PMC9462132

[26]

Liu YF,Qiao XF.Radiomics-based machine learning models for predicting P504s/P63 immunohistochemical expression: a noninvasive diagnostic tool for prostate cancer.Front Oncol2022;12:911426 PMCID:PMC9252170

[27]

Iwamura H,Akamatsu S.Machine learning diagnosis by immunoglobulin N-glycan signatures for precision diagnosis of urological diseases.Cancer Sci2022;113:2434-45 PMCID:PMC9277255

[28]

Wang Y,Shao X.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 Spectrosc2023;293:122426

[29]

Ezenwa EV,Jeje EA.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 Urol2012;10:394-400 PMCID:PMC4442964

[30]

Lenzer J.US expert panel recommends against prostate cancer screening.BMJ2011;343:d6479

[31]

Pandolfo SD,Chung BI.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 Dis2022;1-16:

[32]

Tzelves L,Kalles D.Cluster analysis assessment in proposing a surgical technique for benign prostatic enlargement.Stud Health Technol Inform2022;295:466-9

[33]

Oswald N,Kerr A.Patients want more information after surgery: a prospective audit of satisfaction with perioperative information in lung cancer surgery.J Cardiothorac Surg2018;13:18 PMCID:PMC5796585

[34]

Mourmouris P,Feretzakis G.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 Androl2021;93:418-24

[35]

Lee CL.Current consensus and controversy on the diagnosis of male lower urinary tract symptoms/benign prostatic hyperplasia.Ci Ji Yi Xue Za Zhi2017;29:6-11 PMCID:PMC5509193

[36]

Armah HB.Atypical adenomatous hyperplasia (adenosis) of the prostate: a case report with review of the literature.Diagn Pathol2008;3:34 PMCID:PMC2526076

[37]

Trevethan R.Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice.Front Public Health2017;5:307 PMCID:PMC5701930

[38]

Aristidou A,Topol EJ.Bridging the chasm between AI and clinical implementation.Lancet2022;399:620

[39]

Nagendran M,Lovejoy CA.Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.BMJ2020;368:m689 PMCID:PMC7190037

[40]

Hu X,Mo Y.Progress in artificial intelligence-based prediction of concrete performance.ACT2021;19:924-36

[41]

Nebot JA.A review of artificial intelligent approaches applied to part accuracy prediction.IJMMM2010;8:6

[42]

Bhattacharya I,Vesal S.A review of artificial intelligence in prostate cancer detection on imaging.Ther Adv Urol2022;14:17562872221128791 PMCID:PMC9554123

[43]

Seyyed-Kalantari L,McDermott MBA,Ghassemi M.Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.Nat Med2021;27:2176-82 PMCID:PMC8674135

[44]

Hong C,Wojdyla DM.Predictive Accuracy of stroke risk prediction models across black and white race, sex, and age groups.JAMA2023;329:306-17

[45]

Wang R,Davatzikos C.Bias in machine learning models can be significantly mitigated by careful training: evidence from neuroimaging studies.Proc Natl Acad Sci U S A2023;120:e2211613120 PMCID:PMC9962919

[46]

Istasy P,Iansavichene A.The impact of artificial intelligence on health equity in oncology: scoping review.J Med Internet Res2022;24:e39748 PMCID:PMC9667381

[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]

AI Summary AI Mindmap
PDF

353

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/