Evaluation of Non-Invasive Thermal Imaging for detection of Viability of Onchocerciasis worms

• URL: http://arxiv.org/abs/2203.12620v1
• Date: Wed, 23 Mar 2022 10:06:08 GMT
• Title: Evaluation of Non-Invasive Thermal Imaging for detection of Viability of Onchocerciasis worms
• Authors: Ronak Dedhiya, Siva Teja Kakileti, Goutham Deepu, Kanchana Gopinath, Nicholas Opoku, Christopher King, and Geetha Manjunath
• Abstract summary: Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. This paper proposes use of machine learning over thermal imaging to non-invasively and accurately predict the viability of worms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process. In this paper, we discuss the first-ever study that proposes use of machine learning over thermal imaging to non-invasively and accurately predict the viability of worms. The key contributions of the paper are (i) a unique thermal imaging protocol along with pre-processing steps such as alignment, registration and segmentation to extract interpretable features (ii) extraction of relevant semantic features (iii) development of accurate classifiers for detecting the existence of viable worms in a nodule. When tested on a prospective test data of 30 participants with 48 palpable nodules, we achieved an Area Under the Curve (AUC) of 0.85.

Related papers

• On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions [0.21987601456703473]
  This research introduces an innovative method for the early screening of cardiorespiratory diseases based on an acquisition protocol. We collected, in a clinical setting, a dataset featuring recordings of breathing kinematics obtained by accelerometer and gyroscope readings from five distinct body regions. We propose an end-to-end deep learning pipeline for early cardiorespiratory disease screening, incorporating a preprocessing step segmenting the data into individual breathing cycles.
  arXiv  Detail & Related papers  (2024-08-27T18:29:47Z)
• Detection of Intracranial Hemorrhage for Trauma Patients [1.0074894923170512]
  We propose a novel Voxel-Complete IoU (VC-IoU) loss that encourages the network to learn the 3D aspect ratios of bounding boxes. We extensively experiment on brain bleeding detection using a publicly available dataset, and validate it on a private cohort.
  arXiv  Detail & Related papers  (2024-08-20T12:03:59Z)
• Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy [1.3606622227036818]
  We propose a novel screening method to assess real-time drug efficacy from a time-lapse microscopy video of PDOs. We report better results than other non-time-resolved methods, indicating that the temporality of data is an important factor for the prediction of ATP.
  arXiv  Detail & Related papers  (2023-08-28T09:58:34Z)
• Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia [16.949993123698345]
  Agitation is one of the most prevalent symptoms in people with dementia (PwD) In a previous study, we collected multimodal wearable sensor data from 17 participants for 600 days and developed machine learning models for detecting agitation in one-minute windows. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model.
  arXiv  Detail & Related papers  (2023-02-07T03:14:00Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• A novel optical needle probe for deep learning-based tissue elasticity characterization [59.698811329287174]
  Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography ( OCT) imaging and load sensing at the needle tip.
  arXiv  Detail & Related papers  (2021-09-20T08:29:29Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.