AIMC Topic: Humans

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The impact of deep learning on diagnostic performance in the differentiation of benign and malignant thyroid nodules.

Medical ultrasonography
AIMS: This study aims to use deep learning (DL) to classify thyroid nodules as benign and malignant with ultrasonography (US). In addition, this study investigates the impact of DL on the diagnostic success of radiologists with different experiences....

Machine learning tool for predicting mature oocyte yield and trigger day from start of stimulation: towards personalized treatment.

Reproductive biomedicine online
RESEARCH QUESTION: Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?

Brain tumor detection and segmentation using deep learning.

Magma (New York, N.Y.)
OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection alg...

Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.

Clinical breast cancer
PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model c...

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.

Oncology
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasi...

Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study ai...

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations.

Computational biology and chemistry
Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and ...

External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-reported Overactive Bladder Treatment Outcomes.

Urology
OBJECTIVE: To predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesi...

Artificial Intelligence and cancer: Profile of registered clinical trials.

Journal of cancer policy
Artificial Intelligence (AI) has made significant strides due to advancements in processing algorithms and data availability. Recent years have shown a resurgence in AI, driven by breakthroughs in deep machine learning. AI has attracted particular in...

Histopathologic Differential Diagnosis and Estrogen Receptor/Progesterone Receptor Immunohistochemical Evaluation of Breast Carcinoma Using a Deep Learning-Based Artificial Intelligence Architecture.

The American journal of pathology
In breast carcinoma, invasive ductal carcinoma (IDC) is the most common histopathologic subtype, and ductal carcinoma in situ (DCIS) is a precursor of IDC. These two often occur concomitantly. The immunohistochemical staining of estrogen receptor (ER...