Yashbir Singh

Mayo Clinic - Rochester · Department of Radiology

Primary sclerosing cholangitis (PSC) is a chronic liver disease characterized by inflammation and scarring of the bile ducts, which can lead to cirrhosis and hepatic decompensation. The study aimed to explore the potential value of computational radiomics, a field that extracts quantitative features from medical images, in predicting whether or not...

Background and purpose: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their datasets and/or the number of structures they can identify. This study evaluates the performance of six advanced deep learning models in segme...

In an era of rapid technological progress, this Special Issue aims to provide a comprehensive overview of the state-of-the-art in tomographic imaging [...]

Generative AI is revolutionizing oncological imaging, enhancing cancer detection and diagnosis. This editorial explores its impact on expanding datasets, improving image quality, and enabling predictive oncology. We discuss ethical considerations and introduce a unique perspective on personalized cancer screening using AI-generated digital twins...

Introduction: In the evolving landscape of healthcare andmedicine, themerging of extensive medical datasets with the powerful capabilities of machine learning (ML) models presents a significant opportunity for transforming diagnostics, treatments, and patient care. Methods: This research paper delves into the realm of data-driven healthcare, placin...

Topological data analysis (TDA) uncovers crucial properties of objects in medical imaging. Methods based on persistent homology have demonstrated their advantages in capturing topological features that traditional deep learning methods cannot detect in both radiology and pathology. However, previous research primarily focused on 2D image analysis,...

This editorial explores the emerging role of Graph Filtration Learning (GFL) in revolutionizing Hepatocellular carcinoma (HCC) imaging analysis. As traditional pixel-based methods reach their limits, GFL offers a novel approach to capture complex topological features in medical images. By representing imaging data as graphs and leveraging persisten...

Topological deep learning (TDL) introduces a novel approach to enhancing diagnostic and monitoring processes for metabolic dysfunction-associated fatty liver disease (MAFLD), a condition that is increasingly prevalent globally and a leading cause of liver transplantation. This editorial explores the integration of topology, a branch of mathematics...

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restric...

Background Gastroparesis, characterized by delayed gastric emptying without mechanical obstruction, is a significant complication, especially in diabetic individuals. It manifests through symptoms such as abdominal bloating, feelings of fullness, and pain. This study investigates the prevalence of gastroparesis among non-diabetic and diabetic patie...

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical...

POST GASTROSTOMY INSERTION COMPLICATION IN SHORT TERM

Background: Percutaneous endoscopic gastrostomy (PEG) tube placement is generally safe but is associated with a range of complications. Minor complications include infections, granuloma formation, leakage, and blockages, while major complications encompass aspiration pneumonia, hemorrhage, and more serious conditions such as necrotizing fasciitis a...

Adult ingestion of foreign bodies in the digestive system is a common clinical challenge, often involving mentally impaired individuals, criminals, and drug dealers or occurring accidentally. Encounters with multiple sharp foreign bodies are infrequent and pose significant risks, including gastrointestinal (GI) bleeding, perforation, internal fistu...

Generative Adversarial Networks (GANs) have gained prominence in medical imaging due to their ability to generate realistic images. Traditional GANs, however, often fail to capture intricate topological features such as holes and connectivity components in real images. This study applies TopoGAN, a recently developed model tailored for medical imag...

This article provides an introduction and overview of the mathematical concept of homotopy continuation and its applications-especially for path tracing-in robotic-assisted surgery. It explains the importance of homotopy continuation in solving path-planning and restriction problems and image reconstruction. The opinion article starts with a short...

Scar tissue in the left ventricular (LV) endocardium is a critical factor in the development of malignant ventricular arrhythmias and potentially fatal cardiac outcomes in myocardial infarction patients. This study aimed to assess LV endocardial scar tissue patterns using a Radon descriptor-based machine learning approach. Automated LV segmentation...

Institutional bias can impact patient outcomes, educational attainment, and legal system navigation. Written records often reflect bias, and once bias is identified; it is possible to refer individuals for training to reduce bias. Many machine learning tools exist to explore text data and create predictive models that can search written records to...

Convolutional neural networks (CNNs) have played an important role in medical imaging—from diagnostics to research to data integration. This has allowed clinicians to plan operations, diagnose patients earlier, and study rare diseases in more detail. However, data quality, quantity, and imbalance all pose challenges for CNN training and accuracy; i...

Background: Diagnosis of gastroesophageal reflux disease (GERD) relies on recognizing symptoms of reflux and mucosal changes during esophagogastroduodenoscopy. The desired response to acid suppression therapy is reliable resolution of GERD symptoms; however, these are not always reliable, hence the need for pH testing in unclear cases. Our objectiv...

Objective Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated Left ventr...

Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associate...

Purpose Distinguishing stage 1–2 adrenocortical carcinoma (ACC) and large, lipid poor adrenal adenoma (LPAA) via imaging is challenging due to overlapping imaging characteristics. This study investigated the ability of deep learning to distinguish ACC and LPAA on single time-point CT images. Methods Retrospective cohort study from 1994 to 2022. Im...

Atrial fibrillation (AF) is a common complication in patients who underwent transcatheter aortic valve implantation. Some of these patients have preexisting AF as well. The management of these patients is complex, especially after the procedure, when there is a sudden change in hemodynamics. There are no established guidelines about the management...

Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022. It has shown impressive performance in various domains, including passing exams and creative writing. However, challenges and concerns related to biases and trust persist. In this work, we present a comprehensive review...

Machine learning, and especially deep learning, is rapidly gaining acceptance and clinical usage in a wide range of image analysis applications and is regarded as providing high performance in detecting anatomical structures and identification and classification of patterns of disease in medical images. However, there are many roadblocks to the wid...

Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an...

Background Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in patients with PSC is challenging. Our aim was to extract magnetic resonance imaging (MRI) features that predict the development of hepatic decompensation by applying alg...

Objective: Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated LV segmen...

Objective Atrial Fibrillation (A-fib) is an abnormal heartbeat condition in which the heart races and beats in an uncontrollable way. It is observed that the presence of increased epicardial fat/fatty tissue in the atrium can lead to A-fib. Persistent homology using topological features can be used to recapitulate enormous amounts of spatially comp...

There are increasing concerns about the bias and fairness of artificial intelligence (AI) models as they are put into clinical practice. Among the steps for implementing machine learning tools into clinical workflow, model development is an important stage where different types of biases can occur. This report focuses on four aspects of model devel...

Minimizing bias is critical to adoption and implementation of machine learning (ML) in clinical practice. Systematic mathematical biases produce consistent and reproducible differences between the observed and expected performance of ML systems, resulting in suboptimal performance. Such biases can be traced back to various phases of ML development:...

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias in ML models. Bias can arise at any step of ML creation, including data handling, model development, and performance evaluation. Potential biases in the ML model can be minimized by implementing these steps correctly. This report focuses on perfor...

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one's life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the lef...

In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of...

Using In-house developed deep learning based body Composition model, this research evaluates a machine learning-based combination of topological and radiomics features to predict hepatic decompensation status. Subcutaneous Adipose Tissue (SAT), Skeletal Muscle (SKM), Visceral Adipose Tissue (VAT), and Intermuscular Adipose Tissue (IMAT) were segmen...

Breast cancer metastases are most commonly found in bone, an indication of poor prognosis. Pathway-based biomarkers identification may help elucidate the cellular signature of breast cancer metastasis in bone, further characterizing the etiology and promoting new therapeutic approaches. We extracted gene expression profiles from mouse macrophages f...

Primary sclerosis cholangitis (PSC) predisposes individuals to liver failure, but it is challenging for radiologists examining radiologic images to predict which patients with PSC will ultimately develop liver failure. Motivated by algebraic topology, a topological data analysis - inspired framework was adopted in the study of the imaging pattern b...

Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which irregular heartbeat leads to blood clots, heart failure, stroke, and other significant clinical complications. Researchers have found that the atrial fat can lead to AF in most patients. To develop an automated method for detecting the epicardial fa...

Background The Zebrafish animal model has potential use to study COVID19 infection in-depth due to its genetic similarity with humans. It has antiviral property. As we know, that SARS-CoV-2 is an RNA virus, which has high genetic mutation rate and thus difficult to understand its structure. It is a great way to understand the genetic dynamics of ze...

Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and better evaluation of cardiovascular diseases. Our goal is to apply topology data analysis toward ma...

Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do...

Atrial fibrillation is a disorder in which there is a chaotic fire of electrical signals from the upper chambers of the heart. The identification of the location of the myocardium responsible for firing these signals and ablation of the area may potentially cure the problem. The electrophysiologists may have to insert the probes or catheters and do...

Background Maturation of ultrasound myocardial tissue characterization may have far-reaching implications as a widely available alternative to cardiac magnetic resonance (CMR) for risk stratification in left ventricular (LV) remodeling. Methods We extracted 328 texture-based features of myocardium from still ultrasound images. After we explored th...

Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. The scar formation is an irreversible process of the formation of dead muscle cells that is related to the blockage of a coronary artery. Here, we present a frame...

Minimal invasive device insertion into the soft tissue is a very important medical approach that is helpful for local drug delivery, biopsy, regional anesthesia, blood sampling, and radiofrequency ablation. These methods need to introduce the MID inside the body for exploring the tissue texture pattern. We propose a novel approach by acquiring acou...

Background: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular...

This research develops a framework to segment scar tissue in the LV endocardium wall using computer tomography (CT) with a convolution neural network. The dataset was divided into training images (N = 105) and testing images (N = 44) using the Pixel value classification concept. We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificit...

Atrial fibrillation is a quivering of heartbeat that can lead to blood clots, stroke, heart failure and other heart-related complications. It can affect adults of any age group, but it is very frequent in older people. It affects about 7 in 100 people aged over 65. More men than women have atrial fibrillation. In this work, we used CT images of ten...

Recent studies suggest that central blood pressure(cBP) is more strongly related to cardiovascular events, and many non-invasive devices are available to measure the cBP. Most of them are measured by oscillometric method, yet the most critical one to study the blood pressure waveforms. But due to excessive circuits in these device, which tend to ca...

Atrial fibrillation is a cardiac malfunction characterized by rapid irregular heartbeats which leads to an abnormality of atrial ejection fraction. The radio ablation or medication can aid in blocking the unwanted electrical pathway on the atrium. To assess the post-operative results of these procedures non-invasively through medical images can be...

Medical interventional devices (MIDs) are commonly used to provide access for minimally invasive diagnosis. In this study, We propose the very advanced technique for studying medical interventional devices (MID) with soft tissue interaction by extracting acoustic emission data from the proximal end of the MID. We have to extract dynamical character...

Cardiac disease is a primary cause of death worldwide. Prior study has indicated that the dynamics of the cardiac left ventricle (LV) during diastolic filling is a major indicator of cardiac viability. Hence, studies have aimed to evaluate cardiac health based on quantitative parameters unfolding LV function. In this research, it is demonstrated th...

Cardiac disease is a primary cause of death worldwide. Prior studies have indicated that the dynamics of the cardiac left ventricular (LV) during diastolic filling is a major indicator of cardiac viability. Hence, studies have aimed to evaluate cardiac health based on quantitative parameters unfolding LV function. In this research, it is demonstrat...

Cardiovascular disease can be detected by measuring the regional and global wall motion of the left ventricle (LV) of the heart; In this study, we designed a dynamic simulation tool using Computed Tomography (CT) images to assess the difference between actual and simulated left ventricular functions. Thirteen healthy subjects were involved in the s...

Heart disease can be determined by the calculating regional and global wall motion of the left ventricular (LV). In this research, we designed a dynamic simulation tool using Computed Tomography (CT) images that helps to find the difference between actual and simulated left ventricular functions. In this study, thirteen healthy subjects were involv...

The myocardial contraction creates an effective cardiovascular pumping system. While regional dysfunction of myocardium leads to functional abnormality of cardiac wall motion that is one of the risk factors for ventricular remodeling. It helps in the early phase detecting abnormalities of wall motion while wall motion of cardiac cycle make difficul...