Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating Δ and Δ Measures for Comprehensive Analysis.

Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.

"Where does it hurt?": Exploring EDA Signals to Detect and Localise Acute Pain.

Pain is a highly unpleasant sensory experience, for which currently no objective diagnostic test exists to measure it. Identification and localisation of pain, where the subject is unable to communicate, is a key step in enhancing therapeutic outcomes. Numerous studies have been conducted to categorise pain, but no reliable conclusion has been achieved.

Automatic deep learning-based pleural effusion segmentation in lung ultrasound images.

Background: Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from the clinician to accurately interpret the images. To address this limitation, we previously demonstrated a deep-learning model capable of detecting the presence of PE on LUS at an accuracy greater than 90%, when compared to an experienced LUS operator.

A framework for vehicle quality evaluation based on interpretable machine learning.

Ensuring high quality of a vehicle will increase the lifetime and customer experience, in addition to the maintenance problems, and it is important that there are objective scientific methods available, for evaluating the quality of the vehicle. In this paper, we present a computational framework for evaluating the vehicle quality based on interpretable machine learning techniques. The validation of the proposed framework for a publicly available vehicle quality evaluation dataset has shown an objective machine learning based approach with improved interpretability and deep insight, by using several post-hoc model interpretability enhancement techniques.

Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia.

Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the identification of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets.

A low resource 3D U-Net based deep learning model for medical image analysis.

The success of deep learning, a subfield of Artificial Intelligence technologies in the field of image analysis and computer can be leveraged for building better decision support systems for clinical radiological settings. Detecting and segmenting tumorous tissues in brain region using deep learning and artificial intelligence is one such scenario, where radiologists can benefit from the computer based second opinion or decision support, for detecting the severity of disease, and survival of the subject with an accurate and timely clinical diagnosis. Gliomas are the aggressive form of brain tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect, and often require a combined analysis of different types of radiological scans to make an accurate detection.

Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis.

Lung ultrasound (LUS) imaging as a point-of-care diagnostic tool for lung pathologies has been proven superior to X-ray and comparable to CT, enabling earlier and more accurate diagnosis in real-time at the patient's bedside. The main limitation to widespread use is its dependence on the operator training and experience. COVID-19 lung ultrasound findings predominantly reflect a pneumonitis pattern, with pleural effusion being infrequent.

Automated visibility map of the internal colon surface from colonoscopy video.

Purpose: Optical colonoscopy is a prominent procedure by which clinicians examine the surface of the colon for cancerous polyps using a flexible colonoscope. One of the main concerns regarding the quality of the colonoscopy is to ensure that the whole colonic surface has been inspected for abnormalities. In this paper, we aim at estimating areas that have not been covered thoroughly by providing a map from the internal colon surface.