scholarly journals ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

2021 ◽  
Author(s):  
Tobias Penzkofer ◽  
Anwar R. Padhani ◽  
Baris Turkbey ◽  
Masoom A. Haider ◽  
Henkjan Huisman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Cancer Diagnosis ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Disease Prevalence ◽  
Prostate Cancer Diagnosis ◽  
Detection Systems ◽  
Initial Work

Abstract Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

Artificial Intelligence–assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation

Radiographics ◽  
2021 ◽  
Vol 41 (6) ◽  
pp. 1676-1697
Author(s):  
Lidia Alcalá Mata ◽  
Juan Antonio Retamero ◽  
Rajan T. Gupta ◽  
Roberto García Figueras ◽  
Antonio Luna
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Cancer Diagnosis ◽  
Prostate Cancer Diagnosis ◽  
Pathologic Correlation

scholarly journals Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3944
Author(s):  
Daniele Corradini ◽  
Leonardo Brizi ◽  
Caterina Gaudiano ◽  
Lorenzo Bianchi ◽  
Emanuela Marcelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Future Perspective ◽  
Imaging Data ◽  
Mr Images ◽  
Evaluation Time ◽  
Inter Observer Variability ◽  
Clinically Significant

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

scholarly journals Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis (Preprint)

2021 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Ahmed Al-Hindawi ◽  
Esmita Charani ◽  
Saleh A Alqahtani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Main Concern ◽  
Classification Problems ◽  
End User ◽  
System Usability Scale ◽  
System Usability ◽  
Artificial Intelligence Technology

BACKGROUND The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow. OBJECTIVE Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting. METHODS Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study. RESULTS All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making. CONCLUSIONS Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.

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