TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection

• URL: http://arxiv.org/abs/2305.05105v3
• Date: Sat, 26 Aug 2023 14:45:33 GMT
• Title: TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection
• Authors: Zhenge Jia, Dawei Li, Cong Liu, Liqi Liao, Xiaowei Xu, Lichuan Ping, Yiyu Shi
• Abstract summary: TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial/machine learning (AI/ML) algorithms. This paper first presents the medical problem, dataset and evaluation procedure. It further demonstrates and discusses the designs developed by the leading teams as well as representative results.
• Score: 14.51947401958568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The first ACM/IEEE TinyML Design Contest (TDC) held at the 41st International Conference on Computer-Aided Design (ICCAD) in 2022 is a challenging, multi-month, research and development competition. TDC'22 focuses on real-world medical problems that require the innovation and implementation of artificial intelligence/machine learning (AI/ML) algorithms on implantable devices. The challenge problem of TDC'22 is to develop a novel AI/ML-based real-time detection algorithm for life-threatening ventricular arrhythmia over low-power microcontrollers utilized in Implantable Cardioverter-Defibrillators (ICDs). The dataset contains more than 38,000 5-second intracardiac electrograms (IEGMs) segments over 8 different types of rhythm from 90 subjects. The dedicated hardware platform is NUCLEO-L432KC manufactured by STMicroelectronics. TDC'22, which is open to multi-person teams world-wide, attracted more than 150 teams from over 50 organizations. This paper first presents the medical problem, dataset, and evaluation procedure in detail. It further demonstrates and discusses the designs developed by the leading teams as well as representative results. This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.

Related papers

• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms [0.3069335774032178]
  Single-lead electrocardiogram (ECG) recordings have emerged as a crucial source of information for monitoring cardiovascular health. There has been significant advances in artificial intelligence capable of interpreting these 1-lead ECGs. This design study describes the development of an innovative multiplatform system aimed at the rapid deployment of AI-based ECG solutions.
  arXiv  Detail & Related papers  (2023-10-10T20:33:48Z)
• Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge [18.48059187629883]
  We organized the FLARE 2022 Challenge, the largest abdominal organ analysis challenge to date, to benchmark fast, low-resource, accurate, annotation-efficient, and generalized AI algorithms. We constructed an intercontinental and multinational dataset from more than 50 medical groups, including Computed Tomography (CT) scans with different races, diseases, phases, and manufacturers. Best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89.5%, 90.9%, and 88.3% on North American, European, and Asian cohorts, respectively.
  arXiv  Detail & Related papers  (2023-08-10T21:51:48Z)
• A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC [54.74071593520785]
  Mobile AIGC can be a key enabling technology for an emerging application, called human digital twin (HDT) HDT empowered by the mobile AIGC is expected to revolutionize the personalized healthcare by generating rare disease data, modeling high-fidelity digital twin, building versatile testbeds, and providing 24/7 customized medical services.
  arXiv  Detail & Related papers  (2023-07-22T15:59:03Z)
• Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram [5.183688633606942]
  The George B. Moody PhysioNet Challenge 2022 raised problems of heart murmur detection and related abnormal cardiac function identification from phonocardiograms (PCGs) This work describes the novel approaches developed by our team, Revenger, to solve these problems.
  arXiv  Detail & Related papers  (2023-03-06T09:31:42Z)
• A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries [0.0]
  We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view.
  arXiv  Detail & Related papers  (2022-12-30T01:41:48Z)
• FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy [52.3219875147181]
  Fetoscopic laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS) The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination. Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fet
  arXiv  Detail & Related papers  (2022-06-24T23:44:42Z)
• Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark [48.30502612686276]
  Lung cancer is one of the deadliest cancers, and its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique.
  arXiv  Detail & Related papers  (2022-01-03T03:06:38Z)
• Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines [0.42641920138420947]
  We present our Read, Attend, and Code (RAC) model for learning the medical code assignment mappings. RAC establishes a new state of the art (SOTA) considerably outperforming the current best Macro-F1 by 18.7%. This new milestone marks a meaningful step toward fully autonomous medical coding (AMC) in machines.
  arXiv  Detail & Related papers  (2021-07-10T06:01:58Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.