Symptom Evolution in Individuals with Ongoing Symptomatic COVID-19 and Post COVID-19 Syndrome After SARS-CoV-2 Vaccination Versus Influenza Vaccination.

Background: COVID-19 symptoms may persist beyond acute SARS-CoV-2 infection, as ongoing symptomatic COVID-19 [OSC] (symptom duration 4-12 weeks) and post-COVID syndrome [PCS] (symptom duration ≥12 weeks). Vaccination against SARS-CoV-2 decreases OSC/PCS in individuals subsequently infected with SARS-CoV-2 post-vaccination. Whether vaccination against SARS-CoV-2, or any other vaccinations (such as against influenza) affects symptoms in individuals already experiencing OSC/PCS, more than natural symptom evolution, is unknown.

Uncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

Background: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text.

Evaluating a Smart Textile Loneliness Monitoring System for Older People: Co-Design and Qualitative Focus Group Study.

Background: Previous studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development.

Current State of Community-Driven Radiological AI Deployment in Medical Imaging.

Artificial intelligence (AI) has become commonplace in solving routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. AI has been shown to improve efficiency in medical image generation, processing, and interpretation, and various such AI models have been developed across research laboratories worldwide.

The impact of 3D real-IR delayed post gadolinium MRI parameterisation on the diagnostic performance and optimal descriptor selection in Ménière's disease.

Objectives: To compare the performance and optimal combination of MRI descriptors used for the diagnosis of Ménière's disease (MD) between a real-IR sequence with "zero-point" endolymph (ZPE), and an optimised real-IR sequence with negative signal endolymph (NSE).

Materials And Methods: This retrospective single-centre cross-sectional study evaluated delayed post-gadolinium ZPE and NSE real-IR MRI in consecutive patients with Ménièriform symptoms (8/2020-10/2023). Two observers assessed 14 MRI descriptors.

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesurgery and telementoring. However, its potential to enhance data transmission efficiency and reliability in these fields remains unclear. While previous reviews have explored the general applications of telesurgery and telementoring in specific surgical contexts, this review uniquely focuses on AI models designed to optimise data transmission and mitigate delays.

LoViT: Long Video Transformer for surgical phase recognition.

Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they train spatial feature extractors using frame-level supervision that could lead to incorrect predictions due to similar frames appearing at different phases, and poorly fuse local and global features due to computational constraints which can affect the analysis of long videos commonly encountered in surgical interventions. In this paper, we present a two-stage method, called Long Video Transformer (LoViT), emphasizing the development of a temporally-rich spatial feature extractor and a phase transition map.

Qualitative exploration of the lived experiences of loneliness in later life to inform technology development.

Purpose: Loneliness is a negative emotional state which is common in later life. The accumulative effects of loneliness have a significant impact on the physical and mental health of older adults. We aim to qualitatively explore the experiences of loneliness in later life and identify relevant behaviours and indicators which will inform novel methods of loneliness detection and intervention.

Blood Harmonisation of Endoscopic Transsphenoidal Surgical Video Frames on Phantom Models.

Physical phantom models have been integral to surgical training, yet they lack realism and are unable to replicate the presence of blood resulting from surgical actions. Existing domain transfer methods aim to enhance realism, but none facilitate blood simulation. This study investigates the overlay of blood on images acquired during endoscopic transsphenoidal pituitary surgery on phantom models.

Brain reserve and physical disability in secondary progressive multiple sclerosis.

Background: The brain reserve hypothesis posits that larger maximal lifetime brain growth (MLBG) may confer protection against physical disability in multiple sclerosis (MS). Larger MLBG as a proxy for brain reserve, has been associated with reduced progression of physical disability in patients with early MS; however, it is unknown whether this association remains once in the secondary progressive phase of MS (SPMS). Our aim was to assess whether larger MLBG is associated with decreased physical disability progression in SPMS.

Self-Supervised Anomaly Detection from Anomalous Training Data via Iterative Latent Token Masking.

Anomaly detection and segmentation pose an important task across sectors ranging from medical imaging analysis to industry quality control. However, current unsupervised approaches require training data to not contain any anomalies, a requirement that can be especially challenging in many medical imaging scenarios. In this paper, we propose Iterative Latent Token Masking, a self-supervised framework derived from a robust statistics point of view, translating an iterative model fitting with M-estimators to the task of anomaly detection.

Reduction of surgical complications via 3D models during robotic assisted radical prostatectomy: review of current evidence and meta-analysis.

The use of 3-dimensional (3D) technology has become increasingly popular across different surgical specialities to improve surgical outcomes. 3D technology has the potential to be applied to robotic assisted radical prostatectomy to visualise the patient's prostate anatomy to be used as a preoperative and peri operative surgical guide. This literature review aims to analyse all relevant pre-existing research on this topic.

Realistic morphology-preserving generative modelling of the brain.

Medical imaging research is often limited by data scarcity and availability. Governance, privacy concerns and the cost of acquisition all restrict access to medical imaging data, which, compounded by the data-hungry nature of deep learning algorithms, limits progress in the field of healthcare AI. Generative models have recently been used to synthesize photorealistic natural images, presenting a potential solution to the data scarcity problem.

Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T.

Purpose: T1 mapping and T1-weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools.

MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images.

The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation is extremely expensive and time-consuming. To address this problem, we present MONAI Label, a free and open-source framework that facilitates the development of applications based on artificial intelligence (AI) models that aim at reducing the time required to annotate radiology datasets. Through MONAI Label, researchers can develop AI annotation applications focusing on their domain of expertise.

Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations.

We introduce MHVAE, a deep hierarchical variational autoencoder (VAE) that synthesizes missing images from various modalities. Extending multi-modal VAEs with a hierarchical latent structure, we introduce a probabilistic formulation for fusing multi-modal images in a common latent representation while having the flexibility to handle incomplete image sets as input. Moreover, adversarial learning is employed to generate sharper images.

Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset.

Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging.

Symptoms before and after COVID-19: a population and case-control study using prospective data.

Background: Some individuals experience prolonged illness after acute coronavirus disease 2019 (COVID-19). We assessed whether pre-infection symptoms affected post-acute COVID illness duration.

Methods: Survival analysis was performed in adults (n=23 452) with community-managed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prospectively self-logging data through the ZOE COVID Symptom Study app, at least weekly, from 8 weeks before to 12 weeks after COVID-19 onset, conditioned on presence absence of baseline symptoms (4-8 weeks before COVID-19).

Prospective development study of the Versius Surgical System for use in transoral robotic surgery: an IDEAL stage 1/2a first in human and initial case series experience.

Purpose: Transoral robotic surgery is well established in the treatment paradigm of oropharyngeal pathology. The Versius Surgical System (CMR Surgical) is a robotic platform in clinical use in multiple specialities but is currently untested in the head and neck. This study utilises the IDEAL framework of surgical innovation to prospectively evaluate and report a first in human clinical experience and single centre case series of transoral robotic surgery (TORS) with Versius.

New robotic platform for transoral robotic surgery: an IDEAL stage 0 study.

Objectives: This study aims to assess the feasibility to perform transoral robotic surgery (TORS) with a new robotic platform, the Versius Surgical System (CMR Surgical, UK) in a preclinical cadaveric setting in accordance to stage 0 of the IDEAL-D framework.

Design: IDEAL stage 0 preclinical assessment of the Versius Robotic System in TORS in human cadavers.

Setting: All procedures were performed in a simulated operating theatre environment at a UK surgical training centre.