Akshay S. Chaudhari

Bio: Akshay S. Chaudhari is an academic researcher from Stanford University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 14, co-authored 54 publications receiving 604 citations. Previous affiliations of Akshay S. Chaudhari include University of California, San Diego.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Abstract: ObjectiveWe evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging...

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.

Systems and methods for generating thin image slices from thick image slices

Abstract: Systems and methods for generating thin slice images from thick slice images are disclosed herein. In some examples, a deep learning system may calculate a residual from a thick slice image and add the residual to the thick slice image to generate a thin slice image. In some examples, the deep learning system includes a neural network. In some examples, the neural network may include one or more levels, where one or more of the levels include one or more blocks. In some examples, each level includes a convolution block and a non-linear activation function block. The levels of the neural network may be in a cascaded arrangement in some examples.

Improved Parameters for the Martini Coarse-Grained Protein Force Field

Abstract: The Martini coarse-grained force field has been successfully used for simulating a wide range of (bio)molecular systems. Recent progress in our ability to test the model against fully atomistic force fields, however, has revealed some shortcomings. Most notable, phenylalanine and proline were too hydrophobic, and dimers formed by polar residues in apolar solvents did not bind strongly enough. Here, we reparametrize these residues either through reassignment of particle types or by introducing embedded charges. The new parameters are tested with respect to partitioning across a lipid bilayer, membrane binding of Wimley–White peptides, and dimerization free energy in solvents of different polarity. In addition, we improve some of the bonded terms in the Martini protein force field that lead to a more realistic length of α-helices and to improved numerical stability for polyalanine and glycine repeats. The new parameter set is denoted Martini version 2.2.

Perspective on the Martini model.

Abstract: The Martini model, a coarse-grained force field for biomolecular simulations, has found a broad range of applications since its release a decade ago. Based on a building block principle, the model combines speed and versatility while maintaining chemical specificity. Here we review the current state of the model. We describe recent highlights as well as shortcomings, and our ideas on the further development of the model.