Can artificial intelligence aid the urologists in detecting bladder cancer?
Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urolo...
| Publicado en: | Indian Journal of Urology Vol. 40; no. 4; pp. 221 - 232 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wolters Kluwer India Pvt Ltd
Oct-Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180444514&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180444514 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09701591 1KUG jtl: Indian Journal of Urology issn: 09701591 maglogo: N pubinfo: dt: Oct-Dec2024 vid: 40 iid: 4 pid: 16919 pub: Wolters Kluwer India Pvt Ltd artinfo: ui: 180444514 180444514 180444514 10.4103/iju.iju_39_24 180444514 ppf: 221 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Can artificial intelligence aid the urologists in detecting bladder cancer? aug: au: Hengky, Antoninus Lionardi, Stevan Kristian Kusumajaya, Christopher affil: Department of General Medicine, Fatima Hospital, Ketapang Regency, Indonesia sug: subj: Bladder Neoplasms Diagnosis Artificial Intelligence Utilization Cystoscopy Diagnosis, Computer Assisted Cancer Screening Urologists Human Systematic Review Meta Analysis PubMed Clinical Assessment Tools Data Management Algorithms Data Analysis Software Quality Assessment Sensitivity and Specificity Confidence Intervals ROC Curve Odds Ratio Descriptive Statistics ab: Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions. Methods: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case-control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: "artificial intelligence," "cystoscopy," and "bladder cancer." The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses-Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve. Results: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908-0.976) and 0.957 (95% CI: 0.923-0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42-887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982-0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones. Conclusions: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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