AI-enabled wearable cameras for assisting dietary assessment in African populations.

We have developed a population-level method for dietary assessment using low-cost wearable cameras. Our approach, EgoDiet, employs an egocentric vision-based pipeline to learn portion sizes, addressing the shortcomings of traditional self-reported dietary methods. To evaluate the functionality of this method, field studies were conducted in London (Study A) and Ghana (Study B) among populations of Ghanaian and Kenyan origin.

Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole.

Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after surgery.

Localization and recognition of human action in 3D using transformers.

Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization.

CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading.

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice.

Clinical value of endoscopic ultrasound sound speed in differential diagnosis of pancreatic solid lesion and prognosis of pancreatic cancer.

Background: Differential diagnosis of pancreatic solid lesion (PSL) and prognosis of pancreatic cancer (PC) is a clinical challenge. We aimed to explore the differential diagnostic value of sound speed (SS) obtained from endoscopic ultrasonography (EUS) in PSL and the prognostic value of SS in PC.

Methods: Patients with PSL in The Third Xiangya Hospital of Central South University from March 2019 to October 2019 were prospectively enrolled, who obtained SS from PSL.

Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK.

Background: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs.

Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation.

Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development.

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring.

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g.

Necroptosis in inflammatory bowel disease: A potential effective target.

The morbidity of inflammatory bowel diseases (IBD) is rising rapidly but no curative therapies to prevent its recurrence. Cell death is crucial to maintaining homeostasis. Necroptosis is a newly identified programmed cell death and its roles played in IBD need to be explored.

Antibiotics-induced depression in mice via the microbiota-gut-brain axis.

Background: Intestinal dysbacteriosis is associated with depression. This study aimed to establish an antibiotics-induced depression mouse model and explore the mechanism of antibiotic-induced depression.

Methods: C57BL/6 J mice were treated with antibiotics to prepare the antibiotic-induced depression mouse model.

Can visceral adipose tissue and skeletal muscle predict recurrence of newly diagnosed Crohn's disease in different treatments.

Background And Aims: It is crucial to manage the recurrence of Crohn's disease (CD). This study is aimed to explore whether visceral adipose tissue (VAT) and skeletal muscle (SM) are associated with the recurrence of CD upon different treatments.

Methods: All patients with a definite diagnosis of CD were retrospectively divided into three groups according to distinct treatment regimens: 5-amino salicylic acid group (Group A), steroids + azathioprine (Group B) and biologics (Group C).

Endoscopists' Acceptance on the Implementation of Artificial Intelligence in Gastrointestinal Endoscopy: Development and Case Analysis of a Scale.

Background: The purpose of this paper is to develop and validate a standardized endoscopist acceptance scale for the implementation of artificial intelligence (AI) in gastrointestinal endoscopy.

Methods: After investigating endoscopists who have previously used AI and consulting with AI experts, we developed a provisional scale to measure the acceptance of AI as used in gastrointestinal endoscopy that was then distributed to a sample of endoscopists who have used AI. After analyzing the feedback data collected on the provisional scale, we developed a new formal scale with four factors.

Comparison of Efficacy of Anti-interleukin-17 in the Treatment of Psoriasis Between Caucasians and Asians: A Systematic Review and Meta-Analysis.

Background: Interleukin-17 (IL-17) monoclonal antibody drugs have been increasingly significant in the treatment of psoriasis, but it is not clear whether the efficacy is equivalent across ethnicities.

Objective: To explore the differences of short-term efficacy of IL-17 inhibitors between Caucasians and Asians.

Methods: The pooled log risk ratio (logRR) between the groups was estimated.

Identification of lncRNA expression profiles and analysis of ceRNA in the hippocampus of perinatal glyphosate-exposed mice.

Objective: In order to understand the role of long noncoding RNAs (lncRNAs) played in the mechanisms of glyphosate neurotoxicity in neuronal development.

Methods: Perinatal glyphosate exposure (PGE) mouse model was constructed, and a lncRNA microarray was used to study the lncRNA expression changes in the hippocampus tissue of perinatal glyphosate exposure mice. Then we used GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases to analyze the function of the differentially expressed mRNAs and lncRNAs.

Counting Bites and Recognizing Consumed Food from Videos for Passive Dietary Monitoring.

Assessing dietary intake in epidemiological studies are predominantly based on self-reports, which are subjective, inefficient, and also prone to error. Technological approaches are therefore emerging to provide objective dietary assessments. Using only egocentric dietary intake videos, this work aims to provide accurate estimation on individual dietary intake through recognizing consumed food items and counting the number of bites taken.

Image-Based Food Classification and Volume Estimation for Dietary Assessment: A Review.

A daily dietary assessment method named 24-hour dietary recall has commonly been used in nutritional epidemiology studies to capture detailed information of the food eaten by the participants to help understand their dietary behaviour. However, in this self-reporting technique, the food types and the portion size reported highly depends on users' subjective judgement which may lead to a biased and inaccurate dietary analysis result. As a result, a variety of visual-based dietary assessment approaches have been proposed recently.

Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.

An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious.