Dataset Information

TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.

ABSTRACT: Recent respiratory disease screening studies suggest promising performance of cough classifiers, but potential biases in model training and dataset quality preclude robust conclusions. To examine tuberculosis (TB) cough diagnostic features, we enrolled subjects with pulmonary TB (N = 149) and controls with other respiratory illnesses (N = 46) in Nairobi. We collected a dataset with 33,000 passive coughs and 1600 forced coughs in a controlled setting with similar demographics. We trained a ResNet18-based cough classifier using images of passive cough scalogram as input and obtained a fivefold cross-validation sensitivity of 0.70 (±0.11 SD). The smartphone-based model had better performance in subjects with higher bacterial load {receiver operating characteristic-area under the curve (ROC-AUC): 0.87 [95% confidence interval (CI): 0.87 to 0.88], P < 0.001} or lung cavities [ROC-AUC: 0.89 (95% CI: 0.88 to 0.89), P < 0.001]. Overall, our data suggest that passive cough features distinguish TB from non-TB subjects and are associated with bacterial burden and disease severity.

SUBMITTER: Sharma M

PROVIDER: S-EPMC10776005 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.

Sharma Manuja M Nduba Videlis V Njagi Lilian N LN Murithi Wilfred W Mwongera Zipporah Z Hawn Thomas R TR Patel Shwetak N SN Horne David J DJ

Science advances 20240103 1

Recent respiratory disease screening studies suggest promising performance of cough classifiers, but potential biases in model training and dataset quality preclude robust conclusions. To examine tuberculosis (TB) cough diagnostic features, we enrolled subjects with pulmonary TB (<i>N</i> = 149) and controls with other respiratory illnesses (<i>N</i> = 46) in Nairobi. We collected a dataset with 33,000 passive coughs and 1600 forced coughs in a controlled setting with similar demographics. We tr ...[more]

PMID: 38170773

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Project description:BackgroundThe study objective was to determine whether unlabeled datasets can be used to further train and improve the accuracy of a deep learning system (DLS) for the detection of tuberculosis (TB) on chest radiographs (CXRs) using a two-stage semi-supervised approach.MethodsA total of 111,622 CXRs from the National Institute of Health ChestX-ray14 database were collected. A cardiothoracic radiologist reviewed a subset of 11,000 CXRs and dichotomously labeled each for the presence or absence of potential TB findings; these interpretations were used to train a deep convolutional neural network (DCNN) to identify CXRs with possible TB (Phase I). The best performing algorithm was then used to label the remaining database consisting of 100,622 radiographs; subsequently, these newly-labeled images were used to train a second DCNN (phase II). The best-performing algorithm from phase II (TBNet) was then tested against CXRs obtained from 3 separate sites (2 from the USA, 1 from China) with clinically confirmed cases of TB. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated.ResultsThe phase I algorithm trained using 11,000 expert-labelled radiographs achieved an AUC of 0.88. The phase II algorithm trained on images labeled by the phase I algorithm achieved an AUC of 0.91 testing against a TB dataset obtained from Shenzhen, China and Montgomery County, USA. The algorithm generalized well to radiographs obtained from a tertiary care hospital, achieving an AUC of 0.87; TBNet's sensitivity, specificity, positive predictive value, and negative predictive value were 85%, 76%, 0.64, and 0.9, respectively. When TBNet was used to arbitrate discrepancies between 2 radiologists, the overall sensitivity reached 94% and negative predictive value reached 0.96, demonstrating a synergistic effect between the algorithm's output and radiologists' interpretations.ConclusionsUsing semi-supervised learning, we trained a deep learning algorithm that detected TB at a high accuracy and demonstrated value as a CAD tool by identifying relevant CXR findings, especially in cases that were misinterpreted by radiologists. When dataset labels are noisy or absent, the described methods can significantly reduce the required amount of curated data to build clinically-relevant deep learning models, which will play an important role in the era of precision medicine.

Dataset Information

TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.

Publications

TBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset.

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