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Cataract Disease Detection by Using Transfer Learning-Based Intelligent Methods.

Computational and mathematical methods in medicine
One of the most common visual disorders is cataracts, which people suffer from as they get older. The creation of a cloud on the lens of our eyes is known as a cataract. Blurred vision, faded colors, and difficulty seeing in strong light are the main...

Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images.

Technology in cancer research & treatment
Signet ring cell carcinoma (SRCC) of the stomach is a rare type of cancer with a slowly rising incidence. It tends to be more difficult to detect by pathologists, mainly due to its cellular morphology and diffuse invasion manner, and it has poor prog...

Clinical characteristics and oncological outcomes in negative multiparametric MRI patients undergoing robot-assisted radical prostatectomy.

The Prostate
BACKGROUND: Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant prostate cancers (csPCa). The utility of multiparametric magnetic resonance imaging (mpMRI) in detecting...

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.

Computational and mathematical methods in medicine
Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening tech...

The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia.

The British journal of radiology
OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcrip...

Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
With the development of digital medical technology, ubiquitous smartphones are emerging as valuable tools for the detection of complex and elusive diseases. This paper exploits smartphone walking recording for early detection of Parkinson's disease (...

The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model.

La Radiologia medica
PURPOSE: To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly di...

Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.

Breast cancer research and treatment
PURPOSE: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better un...

Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.

BMJ open gastroenterology
OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an A...

External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.

Journal of breast imaging
OBJECTIVE: There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening ...