Evidence map›Paper›PMID 35913441›Full record

ArticleTurkish journal of urology2022

Current Applications of Artificial Intelligence in Benign Prostatic Hyperplasia.

Milap Shah, Nithesh Naik, Bm Zeeshan Hameed, Rahul Paul, Dasharathraj K Shetty, Sufyan Ibrahim, Bhavan Prasad Rai, Piotr Chlosta, Patrick Rice, Bhaskar K Somani

Open access · diamondAbstract read
In one paragraph

Article in Turkish journal of urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 4 countries.

Milap ShahRobotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi, India ; iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India.
Nithesh NaikiTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Bm Zeeshan HameedDepartment of Urology, Father Muller Medical College, Karnataka, India.
Rahul PaulDepartment of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
Dasharathraj K ShettyDepartment of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Sufyan IbrahimKasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Bhavan Prasad RaiiTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Urology, Freeman Hospital, Newcastle, UK.
Piotr ChlostaDepartment of Urology, Jagiellonian University in Krakow, Kraków, Poland.
Patrick RiceDepartment of Urology, University Hospital Southampton NHS Trust, Southampton, UK.
Bhaskar K SomaniiTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK.
Manipal Academy of Higher Education · INJagiellonian University · PLFather Muller Medical College Hospital · INHarvard University · USMax Healthcare · INUniversity Hospital Southampton NHS Foundation Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is used in predicting the clinical outcomes before minimally invasive treatments for benign prostatic hyperplasia, to address the insufficient reliability despite multiple assessment parameters, such as flow rates and symptom scores. Various models of artificial intelligence and its contemporary applications in benign prostatic hyperplasia are reviewed and discussed. A search strategy adapted to identify and review the literature on the application of artificial intelligence with a dedicated search string with the following keywords: "Machine Learning," "Artificial Intelligence," AND "Benign Prostate Enlargement" OR "BPH" OR "Benign Prostatic Hyperplasia" was included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. In the present review, 1600 patients were included from 4 studies that used different classifiers such as fuzzy systems, computer-based vision systems, and clinical data mining to study the applications of artificial intelligence in diagnoses and severity prediction and determine clinical factors responsible for treatment response in benign prostatic hyperplasia. The accuracy to correctly diagnose benign prostatic hyperplasia by Fuzzy systems was 90%, while that of computer-based vision system was 96.3%. Data mining achieved sensitivity and specificity of 70% and 50%, respectively, in correctly predicting the clinical response to medical treatment in benign prostatic hyperplasia. Artificial intelligence is gaining attraction in urology, with the potential to improve diagnostics and patient care. The results of artificial intelligence-based applications in benign prostatic hyperplasia are promising but lack generalizability of results. However, in the future, we will see a shift in the clinical paradigm as artificial intelligence applications will find their place in the guidelines and revolutionize the decision-making process.

Identifiers

PMID35913441
PMCPMC9612697
OpenAlexW4288096530

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.