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West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 6 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. - 6 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 849-6161
McClellanville Library
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Keith Summey North Charleston Library
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John's Island Library
9 a.m. - 6 p.m.
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Hurd/St. Andrews Library
9 a.m. - 6 p.m.
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Folly Beach Library
9 a.m. - 1 p.m.
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Dorchester Road Library
9 a.m. - 6 p.m.
Phone: (843) 552-6466
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
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Miss Jane's Building (Edisto Library Temporary Location)
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Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case–control study.
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- Author(s): Siebert, Johan N.; Hartley, Mary-Anne; Courvoisier, Delphine S.; Salamin, Marlène; Robotham, Laura; Doenz, Jonathan; Barazzone-Argiroffo, Constance; Gervaix, Alain; Bridevaux, Pierre-Olivier
- Source:
BMC Pulmonary Medicine; 6/1/2023, Vol. 23 Issue 1, p1-16, 16p- Subject Terms:
- Source:
- Additional Information
- Abstract: Background: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. Methods: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. Discussion: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. ClinicalTrials.gov Identifier: NCT05318599. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of BMC Pulmonary Medicine is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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