A pupillary image dataset: 10,000 annotated and 258,790 non-annotated images of patients with glaucoma, diabetes, and subjects influenced by alcohol, coupled with a segmentation performance evaluation.

The Pupillary Light Reflex (PLR) is the involuntary movement of the pupil adapting to lighting conditions. The measurement and qualification of this information have a broad impact in different fields. Thanks to technological advancements and algorithms, obtaining accurate and non-invasive records of pupillary movements is now possible, expanding practical applications.

Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review.

The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians.

Pupillary light reflex as a diagnostic aid from computational viewpoint: A systematic literature review.

This work presents a detailed and complete review of publications on pupillary light reflex (PLR) used to aid diagnoses. These are computational techniques used in the evaluation of pupillometry, as well as their application in computer-aided diagnoses (CAD) of pathologies or physiological conditions that can be studied by observing the movements of miosis and mydriasis of the human pupil. A careful survey was carried out of all studies published over the last 10 years which investigated, electronic devices, recording protocols, image treatment, computational algorithms and the pathologies related to PLR.

Automated Evaluation System for Human Pupillary Behavior.

Analyzing human pupillary behavior is a non-invasive method for evaluating neurological activity. This method contributes to the medical field because changes in pupillary behavior can be correlated with several health conditions such as Parkinson, Alzheimer, autism and diabetes. Analyzing human pupillary behavior is simple and low-cost, and may be used as a complementary diagnosis.

Intraoral radiographs texture analysis for dental implant planning.

Background And Objectives: Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning.

Methods: Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement.