Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues.

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges.

Medical Imaging Applications of Federated Learning.

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body.

Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19.

COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured.

A Forehead Wearable Sensor for the Objective Measurement of Chronic Pain.

Chronic pain impacts one in five Americans and is difficult to manage, costing ~USD 600 billion annually. The subjective experience of pain is a complex processing of central nervous system input. Recent advances in magnetic resonance imaging revealed the prefrontal cortex as vital to the perception of pain and that changes in the cerebral hemodynamics can be used to detect painful sensations.

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing.

FootAssure: A multimodal, in-home wound detection device for diabetic peripheral neuropathy.

Currently, there is no single technology capable of assessing all the multitude of factors associated with peripheral complications of diabetic neuropathy. In this work, a multimodal wound detection system is proposed to help facilitate in-home examinations, utilizing a combination of thermal, multi-spectral 3D imaging modalities. The proposed system is capable of the 3D surface rendering of the foot and would overlay thermal, blood oxygenation, besides other skin health information to aid with foot health monitoring.

Handheld Multispectral Fluorescence Imaging System to Detect and Disinfect Surface Contamination.

Contamination inspection is an ongoing concern for food distributors, restaurant owners, caterers, and others who handle food. Food contamination must be prevented, and zero tolerance legal requirements and damage to the reputation of institutions or restaurants can be very costly. This paper introduces a new handheld fluorescence-based imaging system that can rapidly detect, disinfect, and document invisible organic residues and biofilms which may host pathogens.

Biosensor vital sign detects multiple sclerosis progression.

Objective: To determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.

Methods: Patients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors.

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response.

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity.

Novel MS vital sign: multi-sensor captures upper and lower limb dysfunction.

Objective: To create a novel neurological vital sign and reliably capture MS-related limb disability in less than 5 min.

Methods: Consecutive patients meeting the 2010 MS diagnostic criteria and healthy controls were offered enrollment. Participants completed finger and foot taps wearing the MYO-band© (accelerometer, gyroscope, and surface electromyogram sensors).

Classification and Assessment of Hand Arthritis Stage using Support Vector Machine.

Arthritis is one of the most common health problems affecting people around the world. The goal of the work presented work is to classify and categorizing hand arthritis stages for patients, who may be developing or have developed hand arthritis, using machine learning. Stage classification was done using finger border detection, developed curvature analysis, principal components analysis, support vector machine and K-nearest neighbor algorithms.

A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using shear wave velocity (SWV) with a new machine learning (ML) technique applied to systems from different vendors is comparable to that of magnetic resonance elastography (MRE) in distinguishing non-significant ( View Article and Find Full Text PDF

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla.

A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging.

Purpose: Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data.

Correlation of changes in hemodynamic response as measured by cerebral optical spectrometry with subjective pain ratings in volunteers and patients: a prospective cohort study.

Purpose: Noninvasive cerebral optical spectrometry is a promising candidate technology for the objective assessment physiological changes during pain perception. This study's primary objective was to test if there was a significant correlation between the changes in physiological parameters as measured by a cerebral optical spectrometry-based algorithm (real-time objective pain assessment [ROPA]) and subjective pain ratings obtained from volunteers and laboring women. Secondary aims were performance assessment using linear regression and receiver operating curve (ROC) analysis.

Point Shear Wave Elastography for Grading Liver Fibrosis: Can the Number of Measurements Be Reduced?

The aim of this study was to assess whether the number of liver point shear wave elastography (pSWE) measurements could be reduced compared with the currently recommended 10 valid measurements. Three thousand four hundred one pSWE examinations in patients with liver disease were performed with 10 consecutive valid measurements in liver segment 8. Liver fibrosis grading using published cutoff values were compared retrospectively using the median of 10 versus the first 1-9 measurements with Kendall's τ coefficient.

Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases.

Goal: the objective of this study was to develop a method to identify respiratory phases (i.e., inhale or exhale) of seismocardiogram (SCG) cycles.

Collagen fibers mediate MRI-detected water diffusion and anisotropy in breast cancers.

Collagen 1 (Col1) fibers play an important role in tumor interstitial macromolecular transport and cancer cell dissemination. Our goal was to understand the influence of Col1 fibers on water diffusion, and to examine the potential of using noninvasive diffusion tensor imaging (DTI) to indirectly detect Col1 fibers in breast lesions. We previously observed, in human MDA-MB-231 breast cancer xenografts engineered to fluoresce under hypoxia, relatively low amounts of Col1 fibers in fluorescent hypoxic regions.

Preliminary results on quantification of Seismocardiogram morphological changes, using principal component analysis.

A methodology, based on principal component analysis, is proposed to quantify beat to beat Seismocardiogram changes. The proposed method was tested over a population of 94 subjects including 35 ischemic heart disease patients. The results showed that there was an insignificant overlap between the diseased and the healthy populations in the number of principal components (NPC) and that further development of this method might yield a classification index for myocardial abnormalities.

Hypoxic tumor environments exhibit disrupted collagen I fibers and low macromolecular transport.

Hypoxic tumor microenvironments result in an aggressive phenotype and resistance to therapy that lead to tumor progression, recurrence, and metastasis. While poor vascularization and the resultant inadequate drug delivery are known to contribute to drug resistance, the effect of hypoxia on molecular transport through the interstitium, and the role of the extracellular matrix (ECM) in mediating this transport are unexplored. The dense mesh of fibers present in the ECM can especially influence the movement of macromolecules.

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation.

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data.

Comparative analysis of three different modalities for characterization of the seismocardiogram.

We introduce and compare three different modalities to study seismocardiogram (SCG) and its correlation with cardiac events. We used an accelerometer attached to the subject sternum to get a reference measure. Cardiac events were then approximately identified using echocardiography.