Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Abstract Objectives To assess awareness and knowledge of Interventional Radiology (IR) in a large population of medical students in 2019. Methods An anonymous survey was distributed electronically to 9546 medical students from first to sixth year at three European medical schools. The survey contained 14 questions, including two general questions on diagnostic radiology (DR) and artificial intelligence (AI), and 11 on IR. Responses were analyzed for all students and compared between preclinical (PCs) (first to third year) and clinical phase (Cs) (fourth to sixth year) of medical school. Of 9546 students, 1459 students (15.3%) answered the survey. Results On DR questions, 34.8% answered that AI is a threat for radiologists (PCs: 246/725 (33.9%); Cs: 248/734 (36%)) and 91.1% thought that radiology has a future (PCs: 668/725 (92.1%); Cs: 657/734 (89.5%)). On IR questions, 80.8% (1179/1459) students had already heard of IR; 75.7% (1104/1459) stated that their knowledge of IR wasn’t as good as the other specialties and 80% would like more lectures on IR. Finally, 24.2% (353/1459) indicated an interest in a career in IR with a majority of women in preclinical phase, but this trend reverses in clinical phase. Conclusions Development of new technology supporting advances in artificial intelligence will likely continue to change the landscape of radiology; however, medical students remain confident in the need for specialty-trained human physicians in the future of radiology as a clinical practice. A large majority of medical students would like more information about IR in their medical curriculum; almost a quarter of students would be interested in a career in IR.

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How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

npj Breast Cancer ◽

10.1038/s41523-021-00268-y ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Xinran Wang ◽

Liang Wang ◽

Hong Bu ◽

Ningning Zhang ◽

Meng Yue ◽

...

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Clinical Practice ◽

Subjective Assessment ◽

Current Guideline ◽

Scoring Method ◽

Programmed Death ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

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Liquid Biopsy in Pancreatic Cancer: Are We Ready to Apply It in the Clinical Practice?

Cancers ◽

10.3390/cancers13081986 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1986

Author(s):

Victoria Heredia-Soto ◽

Nuria Rodríguez-Salas ◽

Jaime Feliu

Keyword(s):

Pancreatic Cancer ◽

Clinical Practice ◽

Early Diagnosis ◽

Minimally Invasive ◽

Liquid Biopsy ◽

Genetic Material ◽

Current Status ◽

Ductal Adenocarcinoma ◽

Disease Monitoring ◽

Global Status

Pancreatic ductal adenocarcinoma (PDAC) exhibits the poorest prognosis of all solid tumors, with a 5-year survival of less than 10%. To improve the prognosis, it is necessary to advance in the development of tools that help us in the early diagnosis, treatment selection, disease monitoring, evaluation of the response and prognosis. Liquid biopsy (LB), in its different modalities, represents a particularly interesting tool for these purposes, since it is a minimally invasive and risk-free procedure that can detect both the presence of genetic material from the tumor and circulating tumor cells (CTCs) in the blood and therefore distantly reflect the global status of the disease. In this work we review the current status of the main LB modalities (ctDNA, exosomes, CTCs and cfRNAs) for detecting and monitoring PDAC.

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Artificial intelligence in upper GI endoscopy ‐ current status, challenges and future promise

Journal of Gastroenterology and Hepatology ◽

10.1111/jgh.15354 ◽

2021 ◽

Vol 36 (1) ◽

pp. 20-24

Author(s):

Honggang Yu ◽

Rajvinder Singh ◽

Seon Ho Shin ◽

Khek Yu Ho

Keyword(s):

Artificial Intelligence ◽

Current Status ◽

Gi Endoscopy ◽

Upper Gi ◽

Upper Gi Endoscopy

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Reinforcement Learning for Cloud Computing Digital Library

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.571-572.105 ◽

2014 ◽

Vol 571-572 ◽

pp. 105-108

Author(s):

Lin Xu

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Reinforcement Learning ◽

Digital Library ◽

Learning Algorithms ◽

Experimental Results ◽

Current Status ◽

Self Learning ◽

New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

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Artificial intelligence in cardiovascular CT: Current status and future implications

Journal of Cardiovascular Computed Tomography ◽

10.1016/j.jcct.2021.03.006 ◽

2021 ◽

Author(s):

Andrew Lin ◽

Márton Kolossváry ◽

Manish Motwani ◽

Ivana Išgum ◽

Pál Maurovich-Horvat ◽

...

Keyword(s):

Artificial Intelligence ◽

Current Status ◽

Cardiovascular Ct

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Artificial intelligence and ophthalmology: Current status

Archivos de la Sociedad Española de Oftalmología (English Edition) ◽

10.1016/j.oftale.2021.06.001 ◽

2021 ◽

Vol 96 (8) ◽

pp. 399-400

Author(s):

J. Zarranz-Ventura ◽

C. Bernal-Morales ◽

M. Saenz de Viteri ◽

F.J. Castro Alonso ◽

J.A. Urcola

Keyword(s):

Artificial Intelligence ◽

Current Status

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Minimally Invasive Therapies for Female Stress Urinary Incontinence: the Current Status of Bioinjectables/New Devices (Adjustable Continence Therapy, Urethral Submucosal Collagen Denaturation by Radiofrequency)

The Scientific World JOURNAL ◽

10.1100/tsw.2009.53 ◽

2009 ◽

Vol 9 ◽

pp. 466-478 ◽

Cited By ~ 10

Author(s):

Simone Crivellaro ◽

John J. Smith

Keyword(s):

Clinical Trials ◽

Urinary Incontinence ◽

Clinical Practice ◽

Stress Urinary Incontinence ◽

Minimally Invasive ◽

Emerging Technologies ◽

Current Status ◽

Female Stress Urinary Incontinence ◽

Salvage Procedure ◽

Collagen Denaturation

The aim of this review is to provide an update on the current status of evolving minimally invasive therapies for stress urinary incontinence. Bioinjectables have been available for some time and their current status is reviewed. The adjustable continence device has been used as a salvage procedure for females for a number of years in clinical trials, yet many are unfamiliar with it. Lastly, radiofrequency via a transurethral route has also been utilized in small numbers and will be updated. These later two emerging technologies need further exposure to better define their role in our clinical practice.

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