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

• URL: http://arxiv.org/abs/2212.08624v2
• Date: Mon, 19 Dec 2022 01:56:43 GMT
• Title: Development of A Real-time POCUS Image Quality Assessment and Acquisition Guidance System
• Authors: Zhenge Jia, Yiyu Shi, Jingtong Hu, Lei Yang, Benjamin Nti
• Abstract summary: We will develop a framework to perform real-time AI-assisted quality assessment and probe position guidance to provide training process for novice learners with less manual intervention.
• Score: 16.90302469132399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive care unit. The prior studies demonstrate that AI-assisted software can guide nurses or novices without prior sonography experience to acquire POCUS by recognizing the interest region, assessing image quality, and providing instructions. However, these AI algorithms cannot simply replace the role of skilled sonographers in acquiring diagnostic-quality POCUS. Unlike chest X-ray, CT, and MRI, which have standardized imaging protocols, POCUS can be acquired with high inter-observer variability. Though being with variability, they are usually all clinically acceptable and interpretable. In challenging clinical environments, sonographers employ novel heuristics to acquire POCUS in complex scenarios. To help novice learners to expedite the training process while reducing the dependency on experienced sonographers in the curriculum implementation, We will develop a framework to perform real-time AI-assisted quality assessment and probe position guidance to provide training process for novice learners with less manual intervention.

Related papers

• Evidence Is All You Need: Ordering Imaging Studies via Language Model Alignment with the ACR Appropriateness Criteria [22.897900474995012]
  We introduce a framework to intelligently leverage language models by recommending imaging studies for patient cases aligned with evidence-based guidelines. We make available a novel dataset of patient "one-liner" scenarios to power our experiments, and optimize state-of-the-art language models to achieve an accuracy on par with clinicians in image ordering.
  arXiv  Detail & Related papers  (2024-09-27T23:13:17Z)
• VISION: Toward a Standardized Process for Radiology Image Management at the National Level [3.793492459789475]
  We describe our experiences in establishing a trusted collection of radiology images linked to the United States Department of Veterans Affairs (VA) electronic health record database. Key insights include uncovering the specific procedures required for transferring images from a clinical to a research-ready environment.
  arXiv  Detail & Related papers  (2024-04-29T16:30:24Z)
• AI-Assisted Cervical Cancer Screening [0.7124971549479362]
  Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs) Various handheld devices integrating cameras or smartphones have been recently explored to capture cervical images during VIA and aid decision-making via telemedicine or AI models. We present a novel approach and describe the end-to-end design process to build a robust smartphone-based AI-assisted system that does not require buying a separate integrated device.
  arXiv  Detail & Related papers  (2024-03-18T16:34:38Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users [45.62380752173638]
  When using medical images for diagnosis, it is important that the images are of high quality. In telemedicine, a common problem is that the quality issue is only flagged once the patient has left the clinic, meaning they must return in order to have the exam redone. This paper reports on the development of an AI system for flagging and explaining low-quality medical images in real-time.
  arXiv  Detail & Related papers  (2022-07-06T14:53:26Z)
• Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment [57.419727894848485]
  A computer-aided diagnosis system is required to assist accurate diagnosis from colonoscopy images. Most previous studies at-tempt to develop models for polyp differentiation using Narrow-Band Imaging (NBI) or other enhanced images. We propose a novel framework based on a teacher-student architecture for the accurate colorectal polyp classification.
  arXiv  Detail & Related papers  (2021-08-05T09:31:46Z)
• Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation [62.49837463676111]
  We propose a novel scheme of episodic training with task augmentation on medical imaging classification. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting.
  arXiv  Detail & Related papers  (2021-06-13T03:56:59Z)
• Deep learning for biomedical photoacoustic imaging: A review [0.3234560001579256]
  Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue. extracting relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI.
  arXiv  Detail & Related papers  (2020-11-05T10:33:51Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.