Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.

Purpose: To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.

Material And Methods: A total of 634 fundus images of 317 premature infants born at 23-34 weeks of gestation were evaluated. After image pre-processing, we obtained a rectangular region (ROI).

Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification.

Colorectal cancer is an enormous health concern since it is among the most lethal types of malignancy. The manual examination has its limitations, including subjectivity and data overload. To overcome these challenges, computer-aided diagnostic systems focusing on image segmentation and abnormality classification have been developed.

Retinopathy of Prematurity in Late Preterm Twins with a Birth Weight Discordance: Can it be Predicted by Artificial Intelligence?

Objectives: The objective is to predict the development of retinopathy of prematurity (ROP) in discordant twins using a machine learning approach.

Methods: The records of 640 twin pairs born at 32-35 weeks gestational age (GA) with birth weight (BW) discordance were evaluated retrospectively. The infants' gender, GA, postmenstruel age at examination, BW, discordance rate, ROP Stages and Zones, and treatment options were recorded.

Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs.

Objectives: This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs.

Materials And Methods: The data set used in the retrospective study consisted of different types of upper extremity radiographs obtained from an open-source dataset, with 4,480 images with fractures and 4,383 images without fractures. All fractures involved the ulna or radius.

A fully automated approach involving neuroimaging and deep learning for Parkinson's disease detection and severity prediction.

Three-dimensional magnetic resonance imaging has been proved to detect and predict the severity of progressive neurodegenerative disorders such as Parkinson's disease. The application of pre-processing with neuroimaging methods plays a vital role in post-processing for these problems. The development of technology over the years has enabled the use of deep learning methods such as convolutional neural networks (CNN) on magnetic resonance imaging (MRI) .

Neurodegenerative diseases detection and grading using gait dynamics.

Detection of neurodegenerative diseases such as Parkinson's disease, Huntington's disease, Amyotrophic Lateral Sclerosis, and grading of these diseases' severity have high clinical significance. These tasks based on walking analysis stand out compared to other methods due to their simplicity and non-invasiveness. This study has emerged to realize an artificial intelligence-based disease detection and severity prediction system for neurodegenerative diseases using gait features obtained from gait signals.

CNN-based severity prediction of neurodegenerative diseases using gait data.

Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data.