Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?

Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).

Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.

Machine learning and deep learning for classifying the justification of brain CT referrals.

Objectives: To train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.

Methods: Adult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement.

Retrospective analysis of radiological investigation of surgically excised head and neck lipomas.

Purpose: Differentiating benign lipomas from malignant causes is challenging and preoperative investigative guidelines are not well-defined. The purpose of this study was to retrospectively identify cases of head and neck lipomas that were surgically resected over a 5-year period and to identify the radiological modality chosen and features discussed in the final report. Multidisciplinary outcomes and pathology reports were examined with a view to identifying high risk features of a lipoma to aid in future risk stratification.

Image-guided navigation in posterior orbital tumour surgery: a comparative cohort study.

Purpose: The posterior orbit is a confined space, harbouring neurovascular structures, frequently distorted by tumours. Image-guided navigation (IGN) has the potential to allow accurate localisation of these lesions and structures, reducing collateral damage whilst achieving surgical objectives.

Methods: We assessed the feasibility, effectiveness and safety of using an electromagnetic IGN for posterior orbital tumour surgery via a comparative cohort study.

iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.

Purpose: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy.

Materials And Methods: This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3.

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.

Background And Purpose: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS.

Impact of point-of-care clinical decision support on referrer behavior, imaging volume, patient radiation dose exposure, and sustainability.

Objectives: When referring patients to radiology, it is important that the most appropriate test is chosen to avoid inappropriate imaging that may lead to delayed diagnosis, unnecessary radiation dose, worse patient outcome, and poor patient experience. The current radiology appropriateness guidance standard at our institution is via access to a standalone web-based clinical decision support tool (CDST). A point-of-care (POC) CDST that incorporates guidance directly into the physician workflow was implemented within a subset of head and neck cancer specialist referrers.

Harnessing the Immune System: An Effective Way to Manage Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a heterogenous hematological disorder with malignant potential controlled by immunological characteristics of the tumor microenvironment. Rapid breakthrough in the molecular pathways has made immunological approaches the main anchor in the management of DLBCL, with or without chemotherapeutic agents. Rituximab was the first monoclonal antibody approved for the treatment of DLBCL.