Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes.

Background: It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.

Methods: Our analysis included 8635 participants (52.5% females) aged 40-70 years at baseline from the UK Biobank.

Deep Learning: A Primer for Neurosurgeons.

This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performance, surgical planning, execution, and postoperative assessment. It delves into various deep learning architectures, including convolutional and recurrent neural networks, and their applications in analyzing neuroimaging data for brain tumors, spinal cord injuries, and other neurological conditions. The integration of DL in neurosurgical robotics and the potential for fully autonomous surgical procedures are discussed, highlighting advancements in surgical precision and patient outcomes.

Declaration of Computational Neurosurgery.

Computational neurosurgery is a novel and disruptive field where artificial intelligence and computational modeling are used to improve the diagnosis, treatment, and prognosis of patients affected by diseases of neurosurgical relevance. The field aims to bring new knowledge to clinical neurosciences and inform on the profound questions related to the human brain by applying augmented intelligence, where the power of artificial intelligence and computational inference can enhance human expertise. This transformative field requires the articulation of ethical considerations that will enable scientists, engineers, and clinical neuroscientists, including neurosurgeons, to ensure that the use of such a powerful application is conducted based on the highest moral and ethical standards with a patient-centric approach to predict and prevent mistakes.

Prompt-driven Latent Domain Generalization for Medical Image Classification.

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifact bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem.

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians.

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification.

State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervised domain adaptation) or unlabeled samples (unsupervised domain adaptation). Active learning is a method to select informative samples to obtain maximum performance from minimum annotations.

Multi-Label Generalized Zero Shot Chest Xray Classification By Combining Image-Text Information With Feature Disentanglement.

In fully supervised learning-based medical image classification, the robustness of a trained model is influenced by its exposure to the range of candidate disease classes. Generalized Zero Shot Learning (GZSL) aims to correctly predict seen and novel unseen classes. Current GZSL approaches have focused mostly on the single-label case.

Association of greenspace and natural environment with brain volumes mediated by lifestyle and biomarkers among urban residents.

Objectives: To examine the associaiton between environmental measures and brain volumes and its potential mediators.

Study Design: This was a prospective study.

Methods: Our analysis included 34,454 participants (53.

Association of age at diagnosis of diabetes with subsequent risk of age-related ocular diseases and vision acuity.

Background: The importance of age on the development of ocular conditions has been reported by numerous studies. Diabetes may have different associations with different stages of ocular conditions, and the duration of diabetes may affect the development of diabetic eye disease. While there is a dose-response relationship between the age at diagnosis of diabetes and the risk of cardiovascular disease and mortality, whether the age at diagnosis of diabetes is associated with incident ocular conditions remains to be explored.

Clinical features-based machine learning models to separate sexually transmitted infections from other skin diagnoses.

Introduction: Many sexual health services are overwhelmed and cannot cater for all the individuals who present with sexually transmitted infections (STIs). Digital health software that separates STIs from non-STIs could improve the efficiency of clinical services. We developed and evaluated a machine learning model that predicts whether patients have an STI based on their clinical features.

Metabolomic age and risk of 50 chronic diseases in community-dwelling adults: A prospective cohort study.

It is unclear how metabolomic age is associated with the risk of a wide range of chronic diseases. Our analysis included 110,692 participants (training: n = 27,673; testing: n = 27,673; validating: n = 55,346) aged 39-71 years at baseline (2006-2010) from the UK Biobank. Incident chronic diseases were identified using inpatient records, or death registers until January 2021.

Leading determinants of incident dementia among individuals with and without the apolipoprotein E ε4 genotype: a retrospective cohort study.

Background: Little is known regarding the leading risk factors for dementia/Alzheimer's disease (AD) in individuals with and without APOE4. The identification of key risk factors for dementia/Alzheimer's disease (AD) in individuals with and without the APOE4 gene is of significant importance in global health.

Methods: Our analysis included 110,354 APOE4 carriers and 220,708 age- and sex-matched controls aged 40-73 years at baseline (between 2006-2010) from UK Biobank.

Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.

Purpose: Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary training and validation.

Methods: We proposed a two-stage deep learning-based framework to detect early retinal peripheral degeneration using UWF images from the Chinese Air Force cadets' medical selection between February 2016 and June 2022.

Development and validation of a semi-supervised deep learning model for automatic retinopathy of prematurity staging.

Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios.

Association of antioxidants use with the risk of dementia among community-dwelling adults in the United Kingdom biobank.

Background: Data regarding the association between antioxidant supplementation and incident dementia are limited.

Methods: We included 494,632 adults (54.5% females) aged 40-71 years at baseline from the United Kingdom Biobank in the final analysis.

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification.

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the presence of limited labeled data. Another effective technique to enlarge datasets in a small labeled data regime is data augmentation. An intuitive active learning approach thus consists of combining informative sample selection and data augmentation to leverage their respective advantages and improve the performance of AL systems.

Development and Validation of an Algorithm Model for Predicting Heat Sink Effects during Pulmonary Thermal Ablation.

Aims: The aim of this study was to develop an algorithm model to predict the heat sink effect during thermal ablation of lung tumors and to assist doctors in the formulation and adjustment of surgical protocols.

Background: The heat sink effect is an important factor affecting the therapeutic effect of tumor thermal ablation. At present, there is no algorithm model to predict the intraoperative heat sink effect automatically, which needs to be measured manually, which lacks accuracy and consumes time.

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study.

Purpose: To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children.

Methods: The school-based prospective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementary schools in Mojiang, China. Cycloplegic refraction and biometry were performed at baseline and 4-year visits.

Clinical characteristics and outcomes of patients with post-stroke epilepsy: protocol for an individual patient data meta-analysis from the International Post-stroke Epilepsy Research Repository (IPSERR).

Introduction: Despite significant advances in managing acute stroke and reducing stroke mortality, preventing complications like post-stroke epilepsy (PSE) has seen limited progress. PSE research has been scattered worldwide with varying methodologies and data reporting. To address this, we established the International Post-stroke Epilepsy Research Consortium (IPSERC) to integrate global PSE research efforts.

Effect of vault on predicting postoperative refractive error for posterior chamber phakic intraocular lens based on a machine learning model.

Purpose: To investigate how vault and other biometric variations affect postoperative refractive error of implantable collamer lenses (ICLs) by integrating artificial intelligence and modified vergence formula.

Setting: Eye and ENT Hospital of Fudan University, Shanghai, China.

Design: Artificial intelligence and big data-based prediction model.