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This study in Vietnam assesses the effectiveness of the “Double X” strategy, which combines chest X-ray interpreted by Qure’s AI-powered solution with GeneXpert testing, to screen and diagnose tuberculosis (TB) among vulnerable populations. From 2020 to 2022, 96,631 participants underwent chest X-rays, with 70.2% reporting no TB symptoms. Among 1,144 individuals with Xpert-confirmed TB, 51% were classified as having subclinical TB, meaning they showed no symptoms but had TB-presumptive abnormalities on chest X-rays. Subclinical TB prevalence was found to be higher in older individuals, non-smokers, those previously treated for TB, and in the northern region of Vietnam. The study also analyzed multilabel radiographic abnormalities detected by the AI solutions, finding that fibrosis was significantly associated with higher odds of subclinical TB. The research suggests that the AI solutions may enhance the detection of subclinical TB by identifying key radiographic features, though further studies are needed to confirm these findings.

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Detecting tuberculosis with AI in Vietnam-01 (2).webp

Detecting tuberculosis with AI-powered active case finding in Vietnam

Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world’s deadliest diseases. Most deaths are among persons with TB who are not reached with diagnosis and treatment. Thus, timely screening and accurate detection of TB, particularly using sensitive tools such as chest radiography, is crucial for reducing the global burden of this disease. However, lack of qualified human resources represents a common limiting factor in many high TB-burden countries. Artificial intelligence (AI) has emerged as a powerful complement in many facets of life, including for the interpretation of chest X-ray images. However, while AI may serve as a viable alternative to human radiographers and radiologists, there is a high likelihood that those suffering from TB will not reap the benefits of this technological advance without appropriate, clinically effective use and cost-conscious deployment. The World Health Organization recommended the use of AI for TB screening in 2021, and early adopters of the technology have been using the technology in many ways. In this manuscript, we present a compilation of early user experiences from nine high TB-burden countries focused on practical considerations and best practices related to deployment, threshold and use case selection, and scale-up. While we offer technical and operational guidance on the use of AI for interpreting chest X-ray images for TB detection, our aim remains to maximize the benefit that programs, implementers, and ultimately TB-affected individuals can derive from this innovative technology.

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Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.webp

Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis

Artificial Intelligence (AI) based chest X-ray (CXR) screening for tuberculosis (TB) is becoming increasingly popular. Still, deploying such AI tools can be challenging due to multiple real-life barriers like software installation, workflow integration, network connectivity constraints, limited human resources available to interpret findings, etc. To understand these challenges, PATH implemented a TB REACH active case-finding program in a resource-limited setting of Nagpur in India, where an AI software device (qXR) intended for TB screening using CXR images was used. Eight private CXR laboratories that fulfilled prerequisites for AI software installation were engaged for this program. Key lessons about operational feasibility and accessibility, along with the strategies adopted to overcome these challenges, were learned during this program. This program also helped to screen 10,481 presumptive TB individuals using informal providers based on clinical history. Among them, 2,303 individuals were flagged as presumptive for TB by a radiologist or by AI based on their CXR interpretation. Approximately 15.8% increase in overall TB yield could be attributed to the presence of AI alone because these additional cases were not deemed presumptive for TB by radiologists, but AI was able to identify them. Successful implementation of AI tools like qXR in resource-limited settings in India will require solving real-life implementation challenges for seamless deployment and workflow integration.

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Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India Lessons learned.webp

Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned

Ultra-portable X-ray devices with artificial intelligence (AI) are increasingly used to screen for tuberculosis (TB). Few studies have documented their performance. We aimed to evaluate the performance of chest X-ray (CXR) and symptom screening for active case finding of TB among remote populations using ultra-portable X-ray and AI.

We organized screening camps in rural northeast Nigeria, and all consenting individuals ≥ 15 years were screened for TB symptoms (cough, fever, night sweats, and weight loss) and received a CXR. We used a MinXray Impact system interpreted by AI (qXR V3), which is a wireless setup and can be run without electricity. We collected sputum samples from individuals with an qXR abnormality score of 0.30 or higher or if they reported any TB symptoms. Samples were tested with Xpert MTB/RIF. We documented the TB screening cascade and evaluated the performance of screening with different combinations of symptoms and CXR interpreted by AI.

We screened 5297 individuals during 66 camps: 2684 (51%) were females, and 2613 (49%) were males. Using ≥ 2 weeks of cough to define presumptive TB, 1056 people (20%) would be identified. If a cough of any duration was used, the number with presumptive TB increased to 1889 (36%) and to 3083 (58%) if any of the four symptoms were used. Overall, 769 (14.5%) had abnormality scores of 0.3 or higher, and 447 (8.4%) had a score of 0.5 or higher. We collected 1021 samples for Xpert testing and detected 85 (8%) individuals with TB. Screening for prolonged cough only identified 40% of people with TB. Any symptom detected 90.6% of people with TB, but specificity was 11.4%. Using an AI abnormality score of 0.50 identified 89.4% of people with TB with a specificity of 62.8%.

Ultra-portable CXR can be used to provide more efficient TB screening in hard-to-reach areas. Symptom screening missed large proportions of people with bacteriologically confirmed TB. Employing AI to read CXR can improve triaging when human readers are unavailable and can save expensive diagnostic testing costs.

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Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria.webp

Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria

The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting.&nbsp;

To assess the diagnostic accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons.&nbsp;

Prospective TB screening study in three prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, LunitTB and qXR) and compared their diagnostic accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results.&nbsp;

Among 2,075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with AUCs of 0.87-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity, but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load.&nbsp;

Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB.&nbsp;

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Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons.webp

Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons

There have been few independent evaluations of computer-aided detection (CAD) software for tuberculosis (TB) screening, despite the rapidly expanding array of available CAD solutions. We developed a test library of chest X-ray (CXR) images which was blindly re-read by two TB clinicians with different levels of experience and then processed by 12 CAD software solutions. Using Xpert MTB/RIF results as the reference standard, we compared the performance characteristics of each CAD software against both an Expert and Intermediate Reader, using cut-off thresholds which were selected to match the sensitivity of each human reader. Six CAD systems performed on par with the Expert Reader (Qure.ai, DeepTek, Delft Imaging, JF Healthcare, OXIPIT, and Lunit) and one additional software (Infervision) performed on par with the Intermediate Reader only. Qure.ai, Delft Imaging and Lunit were the only software to perform significantly better than the Intermediate Reader. The majority of these CAD software showed significantly lower performance among participants with a past history of TB. The radiography equipment used to capture the CXR image was also shown to affect performance for some CAD software. TB program implementers now have a wide selection of quality CAD software solutions to utilize in their CXR screening initiatives.&nbsp;

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Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis.webp

Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis

X-ray screening is an important tool in tuberculosis (TB) prevention and care, but access has historically been restricted by its immobile nature. As recent advancements have improved the portability of modern X-ray systems, this study represents an early evaluation of the safety, image quality and yield of using an ultra-portable X-ray system for active case finding (ACF). We reported operational and radiological performance characteristics and compared image quality between the ultra-portable and two reference systems. Image quality was rated by three human readers and by an artificial intelligence (AI) software. We deployed the ultra-portable X-ray alongside the reference system for community-based ACF and described TB care cascades for each system. The ultra-portable system operated within advertised specifications and radiologic tolerances, except on X-ray capture capacity, which was 58% lower than the reported maximum of 100 exposures per charge. The mean image quality rating from radiologists for the ultra-portable system was significantly lower than the reference (3.71 vs. 3.99,&nbsp;p&nbsp;&lt; 0.001). However, we detected no significant differences in TB abnormality scores using the AI software (p&nbsp;= 0.571), nor in any of the steps along the TB care cascade during our ACF campaign. Despite some shortcomings, ultra-portable X-ray systems have significant potential to improve case detection and equitable access to high-quality TB care.&nbsp;

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Early Evaluation of an Ultra-Portable X-ray System for Tuberculosis Active Case Finding.webp

Early Evaluation of an Ultra-Portable X-ray System for Tuberculosis Active Case Finding

Artificial intelligence (AI) algorithms can be trained to&nbsp;recognise&nbsp;tuberculosis-related abnormalities on chest radiographs. Various AI algorithms are available commercially, yet there is little impartial evidence on how their performance compares with each other and with radiologists. We aimed to evaluate five commercial AI algorithms for triaging tuberculosis using a large dataset that had not previously been used to train any AI algorithms.&nbsp;

Individuals aged 15 years or older presenting or referred to three tuberculosis screening&nbsp;centres&nbsp;in Dhaka, Bangladesh, between May 15, 2014, and Oct 4, 2016, were recruited consecutively. Every participant was verbally screened for symptoms and received a digital posterior-anterior chest x-ray and an Xpert MTB/RIF (Xpert) test. All chest x-rays were read independently by a group of three registered radiologists and five commercial AI algorithms: CAD4TB (version 7),&nbsp;InferRead&nbsp;DR (version 2),&nbsp;Lunit&nbsp;INSIGHT CXR (version 4.9.0), JF CXR-1 (version 2), and&nbsp;qXR&nbsp;(version 3). We compared the performance of the AI algorithms with each other, with the radiologists, and with the WHO's Target Product Profile (TPP) of triage tests (≥90% sensitivity and ≥70% specificity). We used a new evaluation framework that simultaneously evaluates sensitivity, proportion of Xpert tests avoided, and number needed to test to inform implementers’ choice of software and selection of threshold abnormality scores.&nbsp;

Chest x-rays from 23 954 individuals were included in the analysis. All five AI algorithms significantly outperformed the radiologists. The areas under the receiver operating characteristic curve were 90·81% (95% CI 90·33–91·29) for&nbsp;qXR, 90·34% (89·81–90·87) for CAD4TB, 88·61% (88·03–89·20) for&nbsp;Lunit&nbsp;INSIGHT CXR, 84·90% (84·27–85·54) for&nbsp;InferRead&nbsp;DR, and 84·89% (84·26–85·53) for JF CXR-1. Only&nbsp;qXR&nbsp;(74·3% specificity [95% CI 73·3–74·9]) and CAD4TB (72·9% specificity [72·3–73·5]) met the TPP at 90% sensitivity. All five AI algorithms reduced the number of Xpert tests required by 50% while maintaining a sensitivity above 90%. All AI algorithms performed worse among older age groups (&gt;60 years) and people with a history of tuberculosis.&nbsp;

AI algorithms can be highly accurate and useful triage tools for tuberculosis detection in high-burden regions, and outperform human readers.&nbsp;

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Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting an evaluation of five artificial intelligence algorithms.webp

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms

The prevalence of diseases other than tuberculosis (TB) detected during chest X-ray screening is poorly described in sub-Saharan Africa. Computer-assisted digital chest X-ray technology is available for TB screening and has the potential to be a screening tool for non-communicable diseases as well. Low- and middle-income countries are in a transition period where the burden of non-communicable diseases is increasing, but health systems are mainly focused on addressing infectious diseases.&nbsp;

Participants were adults undergoing computer-assisted chest X-ray screening for tuberculosis in a community-wide tuberculosis prevalence survey in Blantyre, Malawi. Adults with abnormal radiographs by field radiographer interpretation were evaluated by a physician in a community-based clinic. X-ray classifications were compared to classifications of a random sample of normal chest X-rays by radiographer interpretation. Radiographic features were classified using WHO Integrated Management for Adult Illnesses (IMAI) guidelines. All radiographs taken at the screening tent were analysed by the Qure.ai qXR v2.0 software.&nbsp;

5% (648/13,490) of adults who underwent chest radiography were identified to have an abnormal chest X-ray by the radiographer. 387 (59.7%) of the participants attended the X-ray clinic, and another 387 randomly sampled normal X-rays were available for comparison. Participants who were referred to the community clinic had a significantly higher HIV prevalence than those who had been identified to have a normal CXR by the field radiographer (90 [23.3%] vs. 43 [11.1%]&nbsp;p-value &lt; 0.001). The commonest radiographic finding was cardiomegaly (20.7%, 95% CI 18.0–23.7). One in five (81/387) chest X-rays were misclassified by the radiographer. The overall mean Qure.ai qXR v2.0 score for all reviewed X-rays was 0.23 (SD 0.20). There was a high concordance of cardiomegaly classification between the physician and the computer-assisted software (109/118, 92.4%).&nbsp;

There is a high burden of cardiomegaly on a chest X-ray at a community level, much of which is in patients with diabetes, heart disease and high blood pressure. Cardiomegaly on chest X-ray may be a potential tool for screening for cardiovascular NCDs at the primary care level as well as in the community.&nbsp;

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Pattern of abnormalities amongst chest X-rays study.webp

Pattern of abnormalities amongst chest X-rays of adults undergoing computer-assisted digital chest X-ray screening for tuberculosis in Peri-Urban Blantyre, Malawi: A cross-sectional study

Deep learning is a rising phenomenon in data analysis and one of the ten innovative technologies. Deep learning allows for multi-layered computational models to learn data representations with multiple levels of abstraction. Deep learning provides the best solution to visual object recognition. Deep learning offers the best solution to tuberculosis (TB) detection in X-ray images. In a shortage of qualified radiologists, these new technologies increase the capacity to improve overall TB diagnosis and treatment. This paper aims to review deep learning-based software techniques, such as CAD4TB, qXR, and Lunit INSIGHT used for detecting chest X-ray (CXR) abnormalities. Deep learning-based CAD4TB software highlights the abnormal region in the form of a heat map image. qXR software identifies 15 abnormalities from abnormal chest X-ray images. Lunit INSIGHT CXR software identifies ten abnormalities from chest radiography. The human observer must validate deep learning-based computer-aided detection (CAD) systems used in real diagnosis service. The accuracy of CAD software is analysed by using the receiver operating characteristic (ROC) curve. Deep learning-based systems in medicine must meet ethical values and validate the results.

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Deep Learning-Based Software Tools for Tuberculosis Detection in Chest X-Ray Images.webp

Deep Learning-Based Software Tools for Tuberculosis Detection in Chest X-Ray Images

Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient’s symptoms and signs, laboratory examinations and&nbsp;chest radiography&nbsp;(CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.&nbsp;

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Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening.webp

Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD in different populations, including patients with smear-negative tuberculosis and people living with human immunodeficiency virus (HIV, PLWH).

We collected CXRs and individual patient data (IPD) from studies evaluating CAD in patients self-referring for tuberculosis symptoms with culture or nucleic acid amplification testing as the reference. We reanalyzed CXRs with three CAD programs (CAD4TB version (v) 6, Lunit v3.1.0.0, and qXR v2). We estimated sensitivity and specificity within each study and pooled using IPD meta-analysis. We used multivariable meta-regression to identify characteristics modifying accuracy.

We included CXRs and IPD of 3727/3967 participants from 4/7 eligible studies. 17% (621/3727) were PLWH. 17% (645/3727) had microbiologically confirmed tuberculosis. Despite using the same threshold score for classifying CXR in every study, sensitivity and specificity varied from study to study. The software had similar unadjusted accuracy (at 90% pooled sensitivity, pooled specificities were: CAD4TBv6, 56.9% [95% confidence interval {CI}: 51.7–61.9]; Lunit, 54.1% [95% CI: 44.6–63.3]; qXRv2, 60.5% [95% CI: 51.7–68.6]). Adjusted absolute differences in pooled sensitivity between PLWH and HIV-uninfected participants were: CAD4TBv6, −13.4% [−21.1, −6.9]; Lunit, +2.2% [−3.6, +6.3]; qXRv2: −13.4% [−21.5, −6.6]; between smear-negative and smear-positive tuberculosis was: were CAD4TBv6, −12.3% [−19.5, −6.1]; Lunit, −17.2% [−24.6, −10.5]; qXRv2, −16.6% [−24.4, −9.9]. Accuracy was similar to human readers.

For CAD CXR analysis to be implemented as a high-sensitivity tuberculosis rule-out test, users will need threshold scores identified from their own patient populations and stratified by HIV and smear status.&nbsp;&nbsp;

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Deep Learning-Based A.I. Software for Chest X-Ray Analysis to Detect Microbiologically-Confirmed Tuberculosis A Prospective Study of Diagnostic Accuracy.webp

Deep Learning-Based A.I. Software for Chest X-Ray Analysis to Detect Microbiologically-Confirmed Tuberculosis: A Prospective Study of Diagnostic Accuracy

Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis software meet WHO recommendations for minimal sensitivity and specificity as pulmonary tuberculosis triage tests.

We recruited symptomatic adults at the Indus Hospital, Karachi, Pakistan. We compared two software, qXR version 2.0 (qXRv2) and CAD4TB version 6.0 (CAD4TBv6), with a reference of mycobacterial culture of two sputa. We assessed qXRv2 using its manufacturer prespecified threshold score for chest x-ray classification as tuberculosis present versus not present. For CAD4TBv6, we used a data-derived threshold, because it does not have a prespecified one. We tested for non-inferiority to preset WHO recommendations (0·90 for sensitivity, 0·70 for specificity) using a non-inferiority limit of 0·05. We identified factors associated with accuracy by stratification and logistic regression.

We included 2198 (92·7%) of 2370 enrolled participants. 2187 (99·5%) of 2198 were HIV-negative, and 272 (12·4%) had culture-confirmed pulmonary tuberculosis. For both software, accuracy was non-inferior to WHO-recommended minimum values (qXRv2 sensitivity 0·93 [95% CI 0·89–0·95], non-inferiority p=0·0002; CAD4TBv6 sensitivity 0·93 [95% CI 0·90–0·96], p&lt;0·0001; qXRv2 specificity 0·75 [95% CI 0·73–0·77], p&lt;0·0001; CAD4TBv6 specificity 0·69 [95% CI 0·67–0·71], p=0·0003). Sensitivity was lower in smear-negative pulmonary tuberculosis for both software, and in women for CAD4TBv6. Specificity was lower in men and in those with previous tuberculosis, and reduced with increasing age and decreasing body mass index. Smoking and diabetes did not affect accuracy.

In an HIV-negative population, these software met WHO-recommended minimal accuracy for pulmonary tuberculosis triage tests. Sensitivity will be lower when smear-negative pulmonary tuberculosis is more prevalent.

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Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis – a prospective study of diagnostic accuracy for culture-confirmed disease.webp

Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis – a prospective study of diagnostic accuracy for culture-confirmed disease

Powered by artificial intelligence (AI), particularly deep neural networks, computer aided detection (CAD) tools can be trained to recognize TB-related abnormalities on chest radiographs, thereby screening large numbers of people and reducing the pressure on healthcare professionals. Addressing the lack of studies comparing the performance of different products, we evaluated five AI software platforms specific to TB: CAD4TB (v6), InferReadDR (v2), Lunit INSIGHT for Chest Radiography (v4.9.0) , JF CXR-1 (v2) by and qXR (v3) by on an unseen dataset of chest X-rays collected in three TB screening center in Dhaka, Bangladesh. The 23,566 individuals included in the study all received a CXR read by a group of three Bangladeshi board-certified radiologists. A sample of CXRs were re-read by US board-certified radiologists. Xpert was used as the reference standard. All five AI platforms significantly outperformed the human readers. The areas under the receiver operating characteristic curves are qXR: 0.91 (95% CI:0.90-0.91), Lunit INSIGHT CXR: 0.89 (95% CI:0.88-0.89), InferReadDR: 0.85 (95% CI:0.84-0.86), JF CXR-1: 0.85 (95% CI:0.84-0.85), CAD4TB: 0.82 (95% CI:0.81-0.83). We also proposed a new analytical framework that evaluates a screening and triage test and informs threshold selection through tradeoff between cost efficiency and ability to triage. Further, we assessed the performance of the five AI algorithms across the subgroups of age, use cases, and prior TB history, and found that the threshold scores performed differently across different subgroups. The positive results of our evaluation indicate that these AI products can be useful screening and triage tools for active case finding in high TB-burden regions.

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Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB).webp

Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest x-ray? A multiplatform evaluation of five AI products used for TB screening in a high TB-burden setting

Availability of trained radiologists for fast processing of CXRs in regions burdened with tuberculosis always has been a challenge, affecting both timely diagnosis and patient monitoring. The paucity of annotated images of lungs of TB patients hampers attempts to apply data-oriented algorithms for research and clinical practices. The TB Portals Program database (TBPP, https://TBPortals.niaid.nih.gov) is a global collaboration curating a large collection of the most dangerous, hard-to-cure drug-resistant tuberculosis (DR-TB) patient cases. TBPP, with 1,179 (83%) DR-TB patient cases, is a unique collection that is well positioned as a testing ground for deep learning classifiers. As of January 2019, the TBPP database contains 1,538 CXRs, of which 346 (22.5%) are annotated by a radiologist and 104 (6.7%) by a pulmonologist–leaving 1,088 (70.7%) CXRs without annotations. The Qure.ai qXR artificial intelligence automated CXR interpretation tool, was blind-tested on the 346 radiologist-annotated CXRs from the TBPP database. Qure.ai qXR CXR predictions for cavity, nodule, pleural effusion, hilar lymphadenopathy was successfully matching human expert annotations. In addition, we tested the 12 Qure.ai classifiers to find whether they correlate with treatment success (information provided by treating physicians). Ten descriptors were found as significant: abnormal CXR (p = 0.0005), pleural effusion (p = 0.048), nodule (p = 0.0004), hilar lymphadenopathy (p = 0.0038), cavity (p = 0.0002), opacity (p = 0.0006), atelectasis (p = 0.0074), consolidation (p = 0.0004), indicator of TB disease (p = &lt; .0001), and fibrosis (p = &lt; .0001). We conclude that applying fully automated Qure.ai CXR analysis tool is useful for fast, accurate, uniform, large-scale CXR annotation assistance, as it performed well even for DR-TB cases that were not used for initial training. Testing artificial intelligence algorithms (encapsulating both machine learning and deep learning classifiers) on diverse data collections, such as TBPP, is critically important toward progressing to clinically adopted automatic assistants for medical data analysis.

large_Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis.webp

medium_Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis.webp

small_Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis.webp

thumbnail_Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis.webp

Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis.webp

Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of professionals available to interpret CXR in low-resource, high PTB burden settings. Recently, several computer-aided detection (CAD) programs have been developed to facilitate automated CXR interpretation. We conducted a retrospective case-control study to assess the diagnostic accuracy of a CAD software (qXR, Qure.ai, Mumbai, India) using microbiologically-confirmed PTB as the reference standard. To assess overall accuracy of qXR, receiver operating characteristic (ROC) analysis was used to determine the area under the curve (AUC), along with 95% confidence intervals (CI). Kappa coefficients, and associated 95% CI, were used to investigate inter-rater reliability of the radiologists for detection of specific chest abnormalities. In total, 317 cases and 612 controls were included in the analysis. The AUC for qXR for the detection of microbiologically-confirmed PTB was 0.81 (95% CI: 0.78, 0.84). Using the threshold that maximized sensitivity and specificity of qXR simultaneously, the software achieved a sensitivity and specificity of 71% (95% CI: 66%, 76%) and 80% (95% CI: 77%, 83%), respectively. The sensitivity and specificity of radiologists for the detection of microbiologically-confirmed PTB was 56% (95% CI: 50%, 62%) and 80% (95% CI: 77%, 83%), respectively. For detection of key PTB-related abnormalities ‘pleural effusion’ and ‘cavity’, qXR achieved an AUC of 0.94 (95% CI: 0.92, 0.96) and 0.84 (95% CI: 0.82, 0.87), respectively. For the other abnormalities, the AUC ranged from 0.75 (95% CI: 0.70, 0.80) to 0.94 (95% CI: 0.91, 0.96). The controls had a high prevalence of other lung diseases which can cause radiological manifestations similar to PTB (e.g., 26% had pneumonia, 15% had lung malignancy, etc.). In a tertiary hospital in India, qXR demonstrated moderate sensitivity and specificity for the detection of PTB. There is likely a larger role for CAD software as a triage test for PTB at the primary care level in settings where access to radiologists in limited. Larger prospective studies that can better assess heterogeneity in important subgroups are needed.

large_Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India.webp

medium_Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India.webp

small_Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India.webp

thumbnail_Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India.webp

Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India.webp

Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India

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.

large_Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems.webp

medium_Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems.webp

small_Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems.webp

thumbnail_Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems.webp

Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems.webp

Using artificial intelligence to read chest radiographs for tuberculosis detection – A multi-site evaluation of the diagnostic accuracy of three deep learning systems

Chest X-rays(CXR), being highly sensitive, serve as a Screening tool in TB diagnosis. Though there are no classical features diagnostic of TB on CXR, there are a few patterns that can be used as supportive evidence. In Resource limited settings, developing Deep Learning algorithms for CXR based TB screening, could reduce diagnostic delay. Our algorithm screens for 8 abnormal patterns(TB tags)- Pleural effusion, blunted CP, Atelectasis, Fibrosis, Opacity, Nodules, Calcification and Cavity. It reports ‘No Abnormality Detected’ if none of these patterns are present on CXR.

An anonymized dataset of 423,218 CXRs with matched radiologist reports across (22 models, 9 manufacturers, 166 centres in India) was used to generate training data for the deep learning models. Natural Language Processing techniques were used to extract TB tags from these reports. Deep learning systems were trained to predict the probability of the presence/absence of each TB tag along with heat-maps that highlight abnormal regions in the CXR for each positive result.

We validated the screening algorithm on 3 datasets external to our training set- two public datasets maintained by NIH(from Montgomery and Shenzen) and a third from NIRT, India. The Area under the Receiver Operating Curve (AUC-ROC) for TB prediction was 0.91, 0.87 and 0.83 respectively.

Training on a diversified dataset enabled good performance on samples from completely different demographics. After further validation of it’s robustness against variation, the system can be deployed at scale to improve the current systems for TB screening significantly

large_Efficacy of deep learning for screening pulmonary tuberculosis.webp

medium_Efficacy of deep learning for screening pulmonary tuberculosis.webp

small_Efficacy of deep learning for screening pulmonary tuberculosis.webp

thumbnail_Efficacy of deep learning for screening pulmonary tuberculosis.webp

Efficacy of deep learning for screening pulmonary tuberculosis.webp

Efficacy of deep learning for screening pulmonary tuberculosis

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