AIMC Topic: Pilot Projects
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Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study.
Reproductive biology and endocrinology : RB&E
May 22, 2024
BACKGROUND: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment o...
Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.
Nutrients
May 20, 2024
BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques.
Deep learning for determining the difficulty of endodontic treatment: a pilot study.
BMC oral health
May 17, 2024
BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.
Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.
BMC oral health
May 12, 2024
BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and f...
Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.
Pediatric cardiology
May 9, 2024
Prediction of outcomes following a prenatal diagnosis of congenital heart disease (CHD) is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. We performed a pilot study to train M...
Using machine learning to discover traumatic brain injury patient phenotypes: national concussion surveillance system Pilot.
Brain injury
May 9, 2024
OBJECTIVE: The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles.
Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.
Scientific reports
May 9, 2024
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir...
Assessment of changes in vessel area during needle manipulation in microvascular anastomosis using a deep learning-based semantic segmentation algorithm: A pilot study.
Neurosurgical review
May 9, 2024
Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The ...
Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of : A Pilot Integrative Machine Learning Study.
Depression and anxiety
May 9, 2024
Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide predicti...
Unlocking the future of patient Education: ChatGPT vs. LexiComp® as sources of patient education materials.
Journal of the American Pharmacists Association : JAPhA
May 8, 2024
BACKGROUND: ChatGPT is a conversational artificial intelligence technology that has shown application in various facets of healthcare. With the increased use of AI, it is imperative to assess the accuracy and comprehensibility of AI platforms.
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