Showing papers by "Akshay S. Chaudhari published in 2022"

OpenCap: 3D human movement dynamics from smartphone videos

Abstract: Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing movement dynamics using videos captured from smartphones. OpenCap’s web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap’s practical utility through a 100-subject field study, where a clinician using OpenCap estimated movement dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation

Abstract: Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. While recent machine learning methods for MRI reconstruction and analysis have shown promise for reducing this burden, these techniques are primarily validated with imperfect image quality metrics, which are discordant with clinically-relevant measures that ultimately hamper clinical deployment and clinician trust. To mitigate this challenge, we present the Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset, a collection of quantitative knee MRI (qMRI) scans that enables end-to-end, clinically-relevant evaluation of MRI reconstruction and analysis tools. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. We provide a framework for using qMRI parameter maps, along with image reconstructions and dense image labels, for measuring the quality of qMRI biomarker estimates extracted from MRI reconstruction, segmentation, and detection techniques. Finally, we use this framework to benchmark state-of-the-art baselines on this dataset. We hope our SKM-TEA dataset and code can enable a broad spectrum of research for modular image reconstruction and image analysis in a clinically informed manner. Dataset access, code, and benchmarks are available at https://github.com/StanfordMIMI/skm-tea.

Developing medical imaging AI for emerging infectious diseases

Abstract: Advances in artificial intelligence (AI) and computer vision hold great promise for assisting medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID-19 pandemic, which caused unprecedented stress on healthcare systems around the world, presented what seems to be a perfect opportunity for AI to demonstrate its usefulness. However, of the several hundred medical imaging AI models developed for COVID-19, very few were fit for deployment in real-world settings, and some were potentially harmful. This review aims to examine the strengths and weaknesses of prior studies and provide recommendations for different stages of building useful AI models for medical imaging, among them: needfinding, dataset curation, model development and evaluation, and post-deployment considerations. In addition, this review summarizes the lessons learned to inform the scientific community about ways to create useful medical imaging AI in a future pandemic. Very few of the COVID-19 ML models were fit for deployment in real-world settings. In this Comment, Huang et al. discuss the main steps required to develop clinically useful models in the context of an emerging infectious disease.

Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks

Abstract: We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and subsequently query at arbitrary resolutions. Due to the difficulty of performing super-resolution on inherently noisy data, we analyze network behavior under multiple denoising strategies. Lastly we compare this method to a standard convolutional decoder using both quantitative metrics and a radiologist study implemented in Voxel 1 , our newly developed tool for web-based evaluation of medical images.

Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning

Abstract: Opportunistic computed tomography (CT) analysis is a paradigm where CT scans that have already been acquired for routine clinical questions are reanalyzed for disease prognostication, typically aided by machine learning. While such techniques for opportunistic use of abdominal CT scans have been implemented for assessing the risk of a handful of individual disorders, their prognostic power in simultaneously assessing multiple chronic disorders has not yet been evaluated. In this retrospective study of 9,154 patients, we demonstrate that we can effectively assess 5-year incidence of chronic kidney disease (CKD), diabetes mellitus (DM), hypertension (HT), ischemic heart disease (IHD), and osteoporosis (OST) using single already-acquired abdominal CT scans. We demonstrate that a shared multi-planar CT input, consisting of an axial CT slice occurring at the L3 vertebral level, as well as carefully selected sagittal and coronal slices, enables accurate future disease incidence prediction. Furthermore, we demonstrate that casting this shared CT input into a multi-task approach is particularly valuable in the low-label regime. With just 10% of labels for our diseases of interest, we recover nearly 99% of fully supervised AUROC performance, representing an improvement over single-task learning.

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

Abstract: Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized.

Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

Abstract: . Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task. However, these approaches depend on fully-sampled scans as ground truth data which is either costly or not possible to acquire in many clinical medical imaging applications; hence, reducing dependence on data is desirable. In this work, we propose modeling the proximal operators of unrolled neural networks with scale-equivariant convolutional neural networks in order to improve the data-efficiency and robustness to drifts in scale of the images that might stem from the variability of patient anatomies or change in field-of-view across different MRI scanners. Our approach demonstrates strong improvements over the state-of-the-art unrolled neural networks under the same memory constraints both with and without data augmentations on both in-distribution and out-of-distribution scaled images without significantly increasing the train or inference time.

A method for measuring B0 field inhomogeneity using quantitative double‐echo in steady‐state

Abstract: To develop and validate a method for B0$$ {B}_0 $$ mapping for knee imaging using the quantitative Double‐Echo in Steady‐State (qDESS) exploiting the phase difference ( Δθ$$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two‐gradient‐echo (2‐GRE) method, Δθ$$ \Delta \theta $$ depends only on the first echo time.

Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning

Abstract: Purpose To evaluate the efficacy of two self-supervised learning (SSL) methods (inpainting-based pretext tasks of context prediction and context restoration) for medical image segmentation in label-limited scenarios, and to investigate the effect of implementation design choices for SSL on downstream segmentation performance. Methods Manual segmentation labels were created for 3D knee MRI and 2D abdominal CT datasets. Multiple versions of self-supervised U-Net models were trained to segment tissues in both datasets, each using a different combination of design choices and pretext tasks to determine the effect of different design choices on segmentation performance. The combination of these design choices that resulted in the most significant improvement in Dice score over supervised learning for both datasets was used to train an optimally trained model for segmentation. This model was pretrained on different amounts of unlabeled data to determine the effect of pretraining dataset size on segmentation performance. The highest performing models from this experiment were compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. Results SSL pretraining with context restoration using 32x32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1e-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, pretraining using the maximum amount of unlabeled images resulted in better segmentation performance than pretraining using only the training set (p < 0.05). SSL models pretrained with this amount of data also outperformed baseline supervised models in the computa-tion of clinically-relevant metrics in scenarios with very low amounts of labeled data, especially for challenging classes to segment such as intramuscular adipose tissue on CT images and patellar cartilage on MR images. Conclusion We demonstrate how SSL can overcome paucity of labels for improving tissue segmentation by using unlabeled datasets.