Leveraging cough sounds to optimize chest x-ray usage in low-resource settings

• URL: http://arxiv.org/abs/2402.08789v1
• Date: Tue, 13 Feb 2024 20:54:55 GMT
• Title: Leveraging cough sounds to optimize chest x-ray usage in low-resource settings
• Authors: Alexander Philip, Sanya Chawla, Lola Jover, George P. Kafentzis, Joe Brew, Vishakh Saraf, Shibu Vijayan, Peter Small, Carlos Chaccour
• Abstract summary: We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray.
• Score: 35.63955045151504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chest X-ray is a commonly used tool during triage, diagnosis and management of respiratory diseases. In resource-constricted settings, optimizing this resource can lead to valuable cost savings for the health care system and the patients as well as to and improvement in consult time. We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital (CMCH) in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Collected cough sounds were analyzed using acoustic AI methods. Cross-validation was done on temporal and spectral features on the cough sounds of each patient. Features were summarized using standard statistical approaches. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray. All three methods yielded models that could discriminate to some extent between normal and abnormal with the logistic regression performing best with an area under the receiver operating characteristic curves ranging from 0.7 to 0.78. Despite limitations and its relatively small sample size, this study shows that AI-enabled algorithms can use cough sounds to predict which individuals presenting for chest radiographic examination will have a normal or abnormal results. These results call for expanding this research given the potential optimization of limited health care resources in low- and middle-income countries.

Related papers

• Multi-modal AI for comprehensive breast cancer prognostication [18.691704371847855]
  We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts. Results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.
  arXiv  Detail & Related papers  (2024-10-28T17:54:29Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach? [0.0]
  The Japanese Society of Radiological Technology database was analyzed. Both AI modes -- automated and assisted -- produced an average increase in sensitivity. Both AI modes flagged the pulmonary nodules missed by radiologists in a significant number of cases.
  arXiv  Detail & Related papers  (2022-06-22T08:35:04Z)
• Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection [11.228544549618068]
  Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. We show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization. An evolutionary pruning mechanism is used to train a nodule detection deep learning model on the most informative images from a publicly available lung nodule X-ray dataset.
  arXiv  Detail & Related papers  (2022-01-24T10:08:07Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach [0.0]
  The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79,500 X-Ray images. With the employed methodology, a 91.5% classification accuracy is obtained, with a 87.4% average recall for the worst but most explainable experiment.
  arXiv  Detail & Related papers  (2020-11-29T02:48:39Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting [4.781964929315763]
  We examine the performance of the top 10 performing models on the CheXpert challenge leaderboard. We find that the top 10 chest x-ray models achieve an average AUC of 0.851 on the task of detecting TB on two public TB datasets. Second, we find that the average performance of the models on photos of x-rays (AUC = 0.916) is similar to their performance on the original chest x-ray images. Third, we find that the models tested on an external dataset either perform comparably to or exceed the average performance of radiologists.
  arXiv  Detail & Related papers  (2020-02-26T09:44:21Z)

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.