Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

Abstract

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Preliminary study on miRNA in prostate cancer

“…Most prostate cancer patients are already in advanced cancer stage or even metastatic state at the time of initial diagnosis [10] , and the treatment options for advanced prostate cancer are limited and all patients turn drug resistant after a period of treatment, resulting in poor prognosis for prostate cancer patients [11] .…”

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

“…With a search criterion of the original research paper, published in English, the search engine evaluated information on artificial intelligence and its function in the field of endodontics and included databases like Google Scholar, PubMed, and Science Direct (Rabaan et al, 2022).…”

“…The two types of articles included in PubMed: I reviews and (ii) research papers, were extensively searched using the advanced search option. In the initial stage, the title section was initially searched for keyword strings (Rabaan et al, 2022).…”

“…This enables the service to be offered at a lower cost by requiring less human labour. This review's primary objective is to provide a comprehensive overview of the current AI algorithms utilised in the field of prostate cancer (PC) to diagnose and treat patients (Rabaan et al, 2022).…”

Deep learning assisted identification of ATP5J and ALDH1A2 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer

“…Also, artificial intelligence and machine learning approaches have accelerated the process of biomarker exploration and cancer management. Several studies used artificial intelligence methods such as machine learning and deep learning to analysis of various data including, pathological images and multi-omics data in prostate cancer (6,7). For example, Singhal et al developed a system for the diagnosis of prostate cancer based on images of core needle biopsies (8).…”