32 research outputs found
Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays
Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare
Federated Learning on Heterogenous Data using Chest CT
Large data have accelerated advances in AI. While it is well known that
population differences from genetics, sex, race, diet, and various
environmental factors contribute significantly to disease, AI studies in
medicine have largely focused on locoregional patient cohorts with less diverse
data sources. Such limitation stems from barriers to large-scale data share in
medicine and ethical concerns over data privacy. Federated learning (FL) is one
potential pathway for AI development that enables learning across hospitals
without data share. In this study, we show the results of various FL strategies
on one of the largest and most diverse COVID-19 chest CT datasets: 21
participating hospitals across five continents that comprise >10,000 patients
with >1 million images. We present three techniques: Fed Averaging (FedAvg),
Incremental Institutional Learning (IIL), and Cyclical Incremental
Institutional Learning (CIIL). We also propose an FL strategy that leverages
synthetically generated data to overcome class imbalances and data size
disparities across centers. We show that FL can achieve comparable performance
to Centralized Data Sharing (CDS) while maintaining high performance across
sites with small, underrepresented data. We investigate the strengths and
weaknesses for all technical approaches on this heterogeneous dataset including
the robustness to non-Independent and identically distributed (non-IID)
diversity of data. We also describe the sources of data heterogeneity such as
age, sex, and site locations in the context of FL and show how even among the
correctly labeled populations, disparities can arise due to these biases
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT
The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
Automated brain tumor segmentation methods have become well-established and
reached performance levels offering clear clinical utility. These methods
typically rely on four input magnetic resonance imaging (MRI) modalities:
T1-weighted images with and without contrast enhancement, T2-weighted images,
and FLAIR images. However, some sequences are often missing in clinical
practice due to time constraints or image artifacts, such as patient motion.
Consequently, the ability to substitute missing modalities and gain
segmentation performance is highly desirable and necessary for the broader
adoption of these algorithms in the clinical routine. In this work, we present
the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in
conjunction with the Medical Image Computing and Computer-Assisted Intervention
(MICCAI) 2023. The primary objective of this challenge is to evaluate image
synthesis methods that can realistically generate missing MRI modalities when
multiple available images are provided. The ultimate aim is to facilitate
automated brain tumor segmentation pipelines. The image dataset used in the
benchmark is diverse and multi-modal, created through collaboration with
various hospitals and research institutions.Comment: Technical report of BraSy