Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard. The area under the curve of the three systems was similar: Lunit (0.94, 95% CI: 0.93–0.96), qXR (0.94, 95% CI: 0.92–0.97) and CAD4TB (0.92, 95% CI: 0.90–0.95). When matching the sensitivity of the radiologists, the specificities of the DL systems were significantly higher except for one. Using DL systems to read CXRs could reduce the number of Xpert MTB/RIF tests needed by 66% while maintaining sensitivity at 95% or better. Using a universal cutoff score resulted different performance in each site, highlighting the need to select scores based on the population screened. These DL systems should be considered by TB programs where human resources are constrained, and automated technology is available.
It is almost impossible to talk about the future of medicine without stumbling upon two letters that bring many hopes, fears, and confusions to the topic. Artificial intelligence (AI) is not new but has gained traction in healthcare in the last decade, due in part to advances in deep learning neural networks. Neural networks are a set of algorithms organized in nodes and layers that mimic human cognitive functions, designed to automatically infer rules to recognize patterns1,2. Neural networks help us cluster and classify images, sound, text and time series after being trained on labeled datasets1. Deep-learning networks are distinguished from earlier versions of neural networks by having more than one hidden layer1, so that each layer learns a distinct set of characters and aggregates and combines inputs from the previous layers to understand and perform more complex features and functions, such as reading medical images and autonomous driving1,3.
Deep neural networks provide opportunities for new solutions to tackle tuberculosis (TB), which kills more people world-wide than any single infectious disease4. A major reason for this high mortality is the persistent gap in detection; more than one third of the estimated 10 million incident TB cases are not diagnosed and reported4. Chest x-ray (CXR) has historically been used in TB detection; for mass screenings5, and more recently for prevalence surveys and active case finding interventions6,7. It is recommended by the World Health Organization (WHO) as a triage test prior to the use of Xpert MTB/Rif8. However, CXR is of only limited use for TB diagnosis due to its modest specificity, since many diseases present with similar radiologic patterns9,10, high inter- and intra-reader variability and reproducibility11,12, and the paucity of skilled radiologists in many high TB burden countries12.
Several deep-learning (DL) systems have been developed in recent years to analyze digital chest radiographs for TB-related abnormalities that could potentially address current shortcomings, including reducing human inter-reader variability and reproducibility and supplying radiologic services where radiologists are not available. However, current evidence is limited to only one product, CAD4TB (Delft Imaging Systems, Netherlands)6,13,14 which has been evaluated only with non-DL versions of the software, as DL is new in the current version 6. No peer-reviewed evaluations of the performance of any DL system for detecting TB abnormalities exist, nor do any compare multiple DL systems with human readers. WHO has not made a recommendation on the use of automated reading systems for TB due to the current lack of evidence8. To fill the evidence gap, we compared the performance of three different DL applications in detecting bacteriologically-confirmed TB with that of radiologists experienced in detecting TB, using datasets from two countries.
Zhi Zhen Qin, Melissa S. Sander, Bishwa Rai, Collins N. Titahong, Santat Sudrungrot, Sylvain N. Laah, Lal Mani Adhikari, E. Jane Carter, Lekha Puri, Andrew J. Codlin & Jacob Creswell
Learning Visual Context by Comparison
SRM: A Style-based Recalibration Module for Convolutional Neural Networks
Learning Loss for Active Learning
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
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CBAM: Convolutional Block Attention Module
BAM: Bottleneck Attention Module
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
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Self-Transfer Learning for Fully Weakly Supervised Object Localization
Pixel-Level Domain Transfer
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs
Clinical Validation of a Deep Learning Algorithm for Detection of Pneumonia on Chest Radiographs in Emergency Department Patients with Acute Febrile Respiratory Illness
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Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study
Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs
Deep-learning Based Automated Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs: Diagnostic Performance in Systematic Screening of Asymptomatic Individuals
Performance of a Deep-learning Algorithm Compared to Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population
Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19
Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs
Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration
Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study
Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph
Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs
Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs
Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs
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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
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Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs
Multi-scale Pyramid Pooling for Deep Convolutional Representation
Deep learning computer-aided detection system for pneumonia in febrile neutropenia patients: a diagnostic cohort study
Independent evaluation of 12 artifcial intelligence solutions for the detection of tuberculosis
Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study
Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study
Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs : Case–control study
Extravalidation and reproducibility results of a commercial deep learning-based automatic detection algorithm for pulmonary nodules on chest radiographs at tertiary hospital
Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation
Observer Performance Study to Examine the Feasibility of the AI-powered PD-L1 Analyzer to Assist Pathologists’ Assessment of PD-L1 Expression Using Tumor Proportion Score in Non-Small Cell Lung Cancer
Safety and efficacy of YBL-006, an anti-PD-1 monoclonal antibody in advanced solid tumors: a phase I study