Big Data Analytics in urology: the story so far and the road ahead

The Current Application and Future Potential of Artificial Intelligence in Renal Cancer

Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electronic database to query AI as a method of analysis in kidney cancer research. Key search-words included: Artificial Intelligence, Supervised/Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, radiomics, pathomics, and kidney or renal neoplasms or cancer. 72 clinically relevant and impactful studies related to imaging, histopathology, and outcomes were recognized. We anticipate the incorporation of AI tools into future clinical decision-making for kidney cancer.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Objective

To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.

Methods

A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.

Results

A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.

Conclusion

AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.

The utility of clinical registries for guiding clinical practice in upper tract urothelial cancer: a narrative review

Background and Objective

Upper tract urothelial cancer (UTUC) lacks high-quality evidence to appraise current patterns of presentation, diagnosis, treatment and outcomes as a result of disease rarity and patient heterogeneity. Registries may overcome many of the challenges making clinical trials challenging in UTUC and provide answers to many of the clinical questions that afflict UTUC management. In this narrative review we aim to summarise the design of registries that have contributed to the UTUC literature, discuss their strengths and limitations and the future directions of registries in UTUC.

Methods

Two independent reviewers conducted a search of the OVID MEDLINE database from July 2002-July 2022. Included articles were required to be published in peer reviewed journals and use registry-based methodology to report on UTUC. Search was limited by MeSH and key words and was limited to the English language.

Key Content and Findings

One hundred and forty-four articles were identified and included as reporting on UTUC from a registry-based methodology. Articles utilising registry-based data have substantially increased over the study period with the majority of articles arising from large generalised cancer databases in North America. There has been an increase in UTUC-specific registries in the previous five years that have offered the most granular, complete analysis and these will continue to report in the coming years. The majority of published data assessed epidemiological factors and compared outcomes of treatment modalities with a small proportion of articles focusing on prognostic nomograms and quality of life. Larger cancer registries that contribute the majority of the published analysis are likely subject to significant selection bias when comparing cohorts for treatment analysis and the need for prospective UTUC specific registries is apparent. Future directions include the potential for registry-based randomised controlled trials (RCTs) and clinical quality registries (CQR) that have the ability to change practice and improve care.

Conclusions

The utilisation of registry-based methodology for analysis in UTUC has increased substantially over the last 20 years. In addition to the utilisation of large cancer registries, the creation of UTUC specific registries is likely to contribute the most granular, translatable data in diagnosis and management.

Data Mining in Urology: Understanding Real-world Treatment Pathways for Lower Urinary Tract Systems via Exploration of Big Data

With an increasing number of novel therapeutic options for lower urinary tract symptoms (LUTS), the spectrum of potential treatment pathways resulting from different combinations of treatment decisions is expanding and evolving. Treatment decisions are frequently made with little or no evidence from randomized controlled trials (RCTs) and thus require evidence from other data sources. Clinical routine data reflect real-world treatment pathways. However, evidence for LUTS from routine data means that heterogeneous pathways need to be simultaneously analyzed for compiling evidence in the absence of RCTs. Statistical multi-state model approaches can provide a powerful framework for achieving this goal. More extensive statistical and methodological efforts in the area of similarity of small data are needed to enable the valid pooling of pathways towards joining evidence. PATIENT SUMMARY: Treatment decisions should rely primarily on evidence from clinical trials. When treatment for which there is limited trial evidence needs to be provided, analysis of results from routine clinical practice can represent valuable complementary evidence, but this requires integration of data from heterogeneous treatment pathways.

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.