An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture

D Cembrowska-Lech, A Krzemińska, T Miller… - Biology, 2023 - mdpi.com
Simple Summary The future of plant biology, particularly rapidly advancing precision
horticulture and predictive breeding, will require the transformation of huge volumes of multi …

Magnetic resonance imaging based radiomic models of prostate cancer: A narrative review

A Chaddad, MJ Kucharczyk, A Cheddad, SE Clarke… - Cancers, 2021 - mdpi.com
Simple Summary The increasing interest in implementing artificial intelligence in radiomic
models has occurred alongside advancement in the tools used for computer-aided …

A comparison of various supervised machine learning techniques for prostate cancer prediction

E Erdem, F Bozkurt - Avrupa Bilim ve Teknoloji Dergisi, 2021 - dergipark.org.tr
Prostate cancer is a kind of cancer that is seen worldwide and causes death of many people.
Early diagnosis of cancer helps patients during the treatment phase. For this reason, cancer …

[Retracted] Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

A Alqahtani - Evidence‐Based Complementary and Alternative …, 2022 - Wiley Online Library
Spectacular developments in molecular and cellular biology have led to important
discoveries in cancer research. Despite cancer is one of the major causes of morbidity and …

[HTML][HTML] Application of support vector machine algorithm for early differential diagnosis of prostate cancer

BA Akinnuwesi, KA Olayanju, BS Aribisala… - Data Science and …, 2023 - Elsevier
Prostate cancer (PCa) symptoms are commonly confused with benign prostate hyperplasia
(BPH), particularly in the early stages due to similarities between symptoms, and in some …

Evaluating a machine learning tool for the classification of pathological uptake in whole-body PSMA-PET-CT scans

A Erle, S Moazemi, S Lütje, M Essler, T Schultz… - Tomography, 2021 - mdpi.com
The importance of machine learning (ML) in the clinical environment increases constantly.
Differentiation of pathological from physiological tracer-uptake in positron emission …

[HTML][HTML] Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second …

T Jaouen, R Souchon, PC Moldovan, F Bratan… - Diagnostic and …, 2023 - Elsevier
Purpose The purpose of this study was to develop and test across various scanners a zone-
specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at …

PSA-based machine learning model improves prostate cancer risk stratification in a screening population

M Perera, R Mirchandani, N Papa, G Breemer… - World journal of …, 2021 - Springer
Context The majority of prostate cancer diagnoses are facilitated by testing serum Prostate
Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of …

Machine learning assisted decision support system for prediction of prostrate cancer

MK Mahadi, SR Abir, AM Moon… - 2023 20th …, 2023 - ieeexplore.ieee.org
Over the past several years, there has been a global rise in the prevalence of prostate
cancer. It was discovered that prostate cancer is the most often diagnosed cancer category …

Role of multiparametric prostate MRI in the management of prostate cancer

LP O'Connor, AH Lebastchi, R Horuz… - World Journal of …, 2021 - Springer
Introduction Prostate cancer has traditionally been diagnosed by an elevation in PSA or
abnormal exam leading to a systematic transrectal ultrasound (TRUS)-guided biopsy. This …