Point of Care

Point-of-care (POC) diagnostics aims to bring rapid, accurate medical testing to the patient's bedside, addressing limitations of centralized labs and specialist expertise. Current research heavily utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often augmented with explainable AI (XAI) techniques to improve interpretability and address data scarcity issues. These methods are applied across various modalities, including ultrasound, breath analysis, and even directly from magnetic resonance k-space data, to improve disease detection and aid clinicians in resource-limited settings. The ultimate goal is to enhance healthcare accessibility and efficiency through faster, more reliable diagnoses.

Papers

July 1, 2024

The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data
Ximing Wen, Rosina O. Weber, Anik Sen, Darryl Hannan, Steven C. Nesbit, Vincent Chan, Alberto Goffi, Michael Morris, John C. Hunninghake, Nicholas E. Villalobos, Edward Kim, Christopher J. MacLellan
Global Impact Simple Classifier Binary Classification Ultrasound Image Training Image Scarce Data Quantitative Ultrasound Point of Care

February 29, 2024

Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging
Jennie Karlsson, Marisa Wodrich, Niels Christian Overgaard, Freja Sahlin, Kristina Lång, Anders Heyden, Ida Arvidsson
Distribution Detection Deep Ensemble Softmax Function Ensemble Method Distribution Sample Breast Cancer Classification Point of Care

May 30, 2023

An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics
Ogechukwu Ukwandu, Hanan Hindy, Elochukwu Ukwandu
Medical Image Medical Imaging Lightweight Deep Rapid Diagnosis Accurate Diagnosis Point of Care

January 27, 2023

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
Raghav Singhal, Mukund Sudarshan, Anish Mahishi, Sri Kaushik, Luke Ginocchio, Angela Tong, Hersh Chandarana, Daniel K. Sodickson, Rajesh Ranganath, Sumit Chopra
Common Disease Task Feasibility Disease Classification K Space Accurate Disease Point of Care

January 24, 2023

Diagnosis of Covid-19 Via Patient Breath Data Using Artificial Intelligence
Ozge Doguc, Gokhan Silahtaroglu, Zehra Nur Canbolat, Kailash Hambarde, Ahmet Alperen Yigitbas, Hasan Gokay, Mesut Ylmaz
Artificial Intelligence Data Detection Medical Diagnosis Exhaled Breath Point of Care Breath Sample

January 4, 2023

COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images
Jessy Song, Ashkan Ebadi, Adrian Florea, Pengcheng Xi, Stéphane Tremblay, Alexander Wong
Covid 19 Medical Image Analysis Ultrasound Image High Frequency Display Coronavirus Disease 2019 Lung Ultrasound Point of Care Prototypical Neural Network

December 16, 2022

Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
Zhenge Jia, Yiyu Shi, Jingtong Hu, Lei Yang, Benjamin Nti
Development Activity Point of Care Ultrasound Image Quality

December 6, 2022

MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples
Darryl Hannan, Steven C. Nesbit, Ximing Wen, Glen Smith, Qiao Zhang, Alberto Goffi, Vincent Chan, Michael J. Morris, John C. Hunninghake, Nicholas E. Villalobos, Edward Kim, Rosina O. Weber, Christopher J. MacLellan
Sparse Representation Pneumothorax Segmentation Lung Ultrasound Training Example Point of Care Tiny Classifier

May 18, 2022

COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos
Hilda Azimi, Ashkan Ebadi, Jessy Song, Pengcheng Xi, Alexander Wong
Convolutional Neural Network Covid 19 Spatio Temporal Automatic Diagnosis COVID 19 Case Ultrasound Video Point of Care

April 29, 2022

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning
E. Zhixuan Zeng, Adrian Florea, Alexander Wong
Neural Network Convex Set Decision Support System COVID 19 Case Point of Care Ultrasound Contrastive Learning

Point of Care

Papers

The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data

Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging

An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease

Diagnosis of Covid-19 Via Patient Breath Data Using Artificial Intelligence

COVID-Net USPro: An Open-Source Explainable Few-Shot Deep Prototypical Network to Monitor and Detect COVID-19 Infection from Point-of-Care Ultrasound Images

Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System

MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples

COVID-Net UV: An End-to-End Spatio-Temporal Deep Neural Network Architecture for Automated Diagnosis of COVID-19 Infection from Ultrasound Videos

COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning