AIMC Topic: Proof of Concept Study
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The application of artificial intelligence methods to gene expression data for differentiation of uncomplicated and complicated appendicitis in children and adolescents - a proof of concept study.
BMC pediatrics
Jun 8, 2021
BACKGROUND: Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfull...
Artificial Intelligence in hair research: A proof-of-concept study on evaluating hair assembly features.
International journal of cosmetic science
May 30, 2021
OBJECTIVE: The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The secon...
Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.
Clinical and translational science
May 3, 2021
Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a m...
Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.
The Journal of urology
Apr 21, 2021
PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep lear...
Proof of Concept Study
Image-Guided Biopsy
Magnetic Resonance Imaging, Interventional
Ultrasonography, Interventional
Multiparametric Magnetic Resonance Imaging
Humans
Prostate
Reproducibility of Results
Image Processing, Computer-Assisted
Software
Feasibility Studies
Male
Multimodal Imaging
Prospective Studies
Datasets as Topic
Deep Learning
Prostatic Neoplasms
Retrospective Studies
Time Factors
Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.
Scientific reports
Apr 15, 2021
The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcri...
Deep learning in forensic gunshot wound interpretation-a proof-of-concept study.
International journal of legal medicine
Apr 6, 2021
While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning...
Machine learning is the key to diagnose COVID-19: a proof-of-concept study.
Scientific reports
Mar 30, 2021
The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clin...
Epigenetic Target Fishing with Accurate Machine Learning Models.
Journal of medicinal chemistry
Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.
Communications biology
Mar 19, 2021
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from...
Machine Learning-based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study.
Radiology
Feb 23, 2021
Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonograph...
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