Multi-ancestry polygenic risk scores for the prediction of type 2 diabetes and complications in diverse ancestries.

Background: Polygenic risk scores (PRSs) improve type 2 diabetes (T2D) prediction beyond clinical risk factors but perform poorly in non-European populations, where T2D burden is often higher, undermining their global clinical utility.

Methods: We conducted the largest global effort to date to harmonize T2D genome-wide association study (GWAS) meta-analyses across five ancestries-European (EUR), African/African American (AFR), Admixed American (AMR), South Asian (SAS), and East Asian (EAS)-including 360,000 T2D cases and 1·8 million controls (41% non-EUR). We constructed ancestry-specific and multi-ancestry PRSs in training datasets including 11,000 T2D cases and 32,000 controls, and validated their performance in independent datasets including 39,000 T2D cases and 126,000 controls of diverse ancestries.

Best Practices and Checklist for Reviewing Artificial Intelligence-Based Medical Imaging Papers: Classification.

Recent advances in Artificial Intelligence (AI) methodologies and their application to medical imaging has led to an explosion of related research programs utilizing AI to produce state-of-the-art classification performance. Ideally, research culminates in dissemination of the findings in peer-reviewed journals. To date, acceptance or rejection criteria are often subjective; however, reproducible science requires reproducible review.

Clinical, molecular and radiological predictors of prognosis in newly diagnosed astrocytoma, IDH-mutant, WHO grade 4.

Background: Astrocytoma, isocitrate dehydrogenase-mutant, WHO grade 4 (Astro4), is a new tumor type in the 2021 WHO classification of central nervous system tumors that has been poorly characterized in the literature. This study evaluates predictors of prognosis in a large cohort of newly diagnosed Astro4.

Methods: We retrospectively identified 128 consecutive adult patients who presented with an initial diagnosis of Astro4 at Dana-Farber Cancer Institute and Massachusetts General Hospital between 2010 and 2021.

External Validation of a Winning Artificial Intelligence Algorithm from the RSNA 2022 Cervical Spine Fracture Detection Challenge.

Background And Purpose: The Radiological Society of North America has actively promoted artificial intelligence (AI) challenges since 2017. Algorithms emerging from the recent RSNA 2022 Cervical Spine Fracture Detection Challenge demonstrated state-of-the-art performance in the competition's data set, surpassing results from prior publications. However, their performance in real-world clinical practice is not known.

Ivosidenib for the treatment of IDH1-mutant glioma, grades 2-4: Tolerability, predictors of response, and outcomes.

Background: Mutant isocitrate dehydrogenase (IDHm) inhibitors represent a novel targeted approach for treating IDHm glioma patients, yet their optimal use in clinical practice outside of clinical trials remains undefined. This study describes the real-world utilization of the mutant IDH1 inhibitor (IDHi), ivosidenib, in patients with IDHm glioma.

Methods: We retrospectively reviewed clinical and radiographic data from patients with IDHm glioma treated with ivosidenib monotherapy from 2020 to 2024 at the Dana-Farber Cancer Institute and Massachusetts General Hospital.

Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center.

Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1828 CT scans (from 1829 series: 130 positive for fracture, 1699 negative for fracture; 1308 noncontrast, 521 contrast enhanced) from 1779 patients (mean age, 55.8 years ± 22.

Artificial intelligence in neuroimaging of brain tumors: reality or still promise?

Purpose Of Review: To provide an updated overview of artificial intelligence (AI) applications in neuro-oncologic imaging and discuss current barriers to wider clinical adoption.

Recent Findings: A wide variety of AI applications in neuro-oncologic imaging have been developed and researched, spanning tasks from pretreatment brain tumor classification and segmentation, preoperative planning, radiogenomics, prognostication and survival prediction, posttreatment surveillance, and differentiating between pseudoprogression and true disease progression. While earlier studies were largely based on data from a single institution, more recent studies have demonstrated that the performance of these algorithms are also effective on external data from other institutions.

Evaluating progress in automatic chest X-ray radiology report generation.

Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation.

Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography.

Purpose: To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance.

Materials And Methods: A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients.

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases.

Efficient labeling of retinal fundus photographs using deep active learning.

Purpose: To compare the performance of four deep active learning (DAL) approaches to optimize label efficiency for training diabetic retinopathy (DR) classification deep learning models.

Approach: 88,702 color retinal fundus photographs from 44,351 patients with DR grades from the publicly available EyePACS dataset were used. Four DAL approaches [entropy sampling (ES), Bayesian active learning by disagreement (BALD), core set, and adversarial active learning (ADV)] were compared to conventional naive random sampling.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

Objectives: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.

Methods: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal.

A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION.

Purpose: To survey the current literature regarding applications of deep learning to optical coherence tomography in age-related macular degeneration (AMD).

Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review was conducted from January 1, 2000, to May 9, 2021, using PubMed and EMBASE databases. Original research investigations that applied deep learning to optical coherence tomography in patients with AMD or features of AMD (choroidal neovascularization, geographic atrophy, and drusen) were included.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10), which were delineated to 338 distinct association signals.

Federated Deep Learning to More Reliably Detect Body Part for Hanging Protocols, Relevant Priors, and Workflow Optimization.

Preparing radiology examinations for interpretation requires prefetching relevant prior examinations and implementing hanging protocols to optimally display the examination along with comparisons. Body part is a critical piece of information to facilitate both prefetching and hanging protocols, but body part information encoded using the Digital Imaging and Communications in Medicine (DICOM) standard is widely variable, error-prone, not granular enough, or missing altogether. This results in inappropriate examinations being prefetched or relevant examinations left behind; hanging protocol optimization suffers as well.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection.

Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown.

Deep Learning for Pulmonary Embolism Detection: Tackling the RSNA 2020 AI Challenge.

In 2020, the Radiological Society of North America and Society of Thoracic Radiology sponsored a machine learning competition to detect and classify pulmonary embolism (PE). This challenge was one of the largest of its kind, with more than 9000 CT pulmonary angiography examinations comprising almost 1.8 million expertly annotated images.

Deep Learning: An Update for Radiologists.

Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data.

SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms.

Purpose: Measurements of breast arterial calcifications (BAC) can offer a personalized, non-invasive approach to risk-stratify women for cardiovascular diseases such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications.

Methods: To separate BAC in mammograms, we propose a lightweight fine vessel segmentation method Simple Context U-Net (SCU-Net).

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

Objectives: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.