PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

Abstract

Background and Objective Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine. Methods We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools. Results The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians. Conclusion This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

Artificial Intelligence Applications in Health Care Practice: Scoping Review

“…. Most (33/45, 73%) of the articles were from North America and Europe[46,47,[49][50][51][52][53][54][55]57,58,[61][62][63][67][68][69][70][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87], of which most (18/33, 55%) were from the United States[46,47,49-52,54,68,73-77, 79-81,84,87]. The greatest number of AI systems were implemented either in hospital-wide settings (6/45, 13%)[50,55,56,65,74,80] or in radiology (6/45, 13%)[53,56,66,68,73,76].…”

“…59,61,62,64,67,69,70,72,74,75,[78][79][80]82,[86][87][88][89], with clinical and IT or informatics backgrounds being the most common combination (9/27, 33%)[47,50,55,61,70,74,79,87,89]. Among studies with authors from only 1 domain, the most common background was clinical (8/45, 18%)[63,65,66,68,71,73,76,84].…”

Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences

“…The four fields of intervention, innovation, implementation, and improvement sciences have generated a wealth of oftenoverlapping knowledge, some of which we propose have considerable relevance for understanding and/or facilitating the use of AI in healthcare (Table 1). Thus far, AI research has largely focused on engineering, computer science and programming (12)(13)(14). However, this research does not ascertain the adoption and subsequent use of AI in everyday healthcare.…”

“…AI has predominantly been viewed through a rather narrow technology-centric lens, with research focusing on the design of the technology and its interaction with the immediate users (12)(13)(14). However, some of the major challenges in AI are faced in the "last mile" of the AI research and development (R&D) process, i.e., the implementation and routine use of AI-based applications in clinical settings (15,16).…”

Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening

“…To provide physicians a holistic view of patients’ medical conditions, a seamless exchange of information across systems and departments is required [ 8 – 10 ]. Effective data integration tools that extract and combine data from multiple sources are supported by integration of best practice guidelines is needed to ensure continuous interdisciplinary patient care [ 6 , 11 – 13 ]. Moreover, data representation must be adapted to the individual needs and choices of each recipient and its purpose in the care supply chain to avoid missing or overwhelming information [ 6 ].…”