Patient demographicsA total of 4495 participants were prospectively enrolled from January 2018 to November 2023 across 12 primary health care centers and one tertiary care center. From the initial cohort of 4495 participants, 132 individuals were excluded from the analysis for various reasons. Among these exclusions, 81 individuals reported an unproductive cough without an available bronchoalveolar lavage (BAL) sample, while 29 participants declined to participate in the study. Additionally, 4 individuals had a documented history of previous TB treatment, and 17 participants were pregnant. However, 1 individual’s inclusion was deemed inconclusive due to uncertain results from either the CXR or GeneXpert MTB/RIF test (refer to Fig. 1). A total of 4363 individuals were ultimately included in the analysis. Among the 4363 individuals included in the study, 680 had an unproductive cough, but their BAL fluid was accessible and available for analysis. The median age of the participants was 43.1 years, and 2161 males (49.6%) and 2202 females (50.4%) were included. Predominantly, fever symptoms were evident in the majority of participants, accounting for 2565 individuals (58.8%) (refer to Table 1).Fig. 1Workflow for evaluating the performance of DecXpert CAD software. From the initially enrolled 4495 individuals, 4364 participants were included in the analysis after excluding those with no chest X-ray and/or inconclusive GeneXpert MTB/RIF results. The GeneXpert MTB/RIF test results categorized participants as TB(+) (2345) or TB(–) (2018). Radiologists interpreted the chest X-rays, identifying 1665 as TB(+) and 1695 as TB(–). The DecXpert CAD system classified 2064 cases as TB(+) and 1716 as TB(–). The performance metrics calculated include positive predictive value (PPV) and negative predictive value (NPV) for each method, using the GeneXpert MTB/RIF results as the reference standard.Table 1 Demographic and clinical characteristics of the study population stratified by GeneXpert, radiology, and DecXpert test results.Hemoptysis and night sweats were reported by 437 (10%) and 306 (7%) participants, respectively. Among the individuals in the study cohort, 2345 individuals (53.7%) were confirmed to be TB positive, while 2018 individuals (46.3%) tested negative for TB (refer to Fig. 1). Notably, within the subgroup with positive TB results, there were 2161 (49.6%) males and 2202 (50.4%) females (Table 1). Gender, fever, cough, hemoptysis, and night sweats exhibited significant associations (P-value < 0.05) with the GeneXpert MTB/RIF test results (refer to Table 1). Moreover, gender, age, hemoptysis, night sweats and cough demonstrated significant associations (P-value < 0.01) with the radiological and DecXpert results (refer to Table 1).Radiologists demonstrated a positive predictive value (PPV) of 71%, suggesting that out of the 2345 individuals identified as positive by GeneXpert MTB/RIF, 1665 individuals were confirmed positive by radiologists (refer to Table 2). In contrast, DecXpert exhibited a higher PPV of 88%, where out of the 2345 individuals identified as positive by GeneXpert MTB/RIF, 2064 were subsequently confirmed as positive by DecXpert. Regarding the negative predictive value (NPV), radiologists achieved an 83.9% NPV. This indicates that among the 2018 individuals classified as negative by GeneXpert MTB/RIF, 1695 were confirmed as negative by radiologists. Conversely, DecXpert exhibited a higher negative predictive value (NPV) of 85%, suggesting that of the 2018 individuals classified as negative for TB by GeneXpert MTB/RIF, 1716 were confirmed as negative by DecXpert (refer to Table 2).Table 2 Performance evaluation of DecXpert against GeneXpert MTB/RIF and radiologists.Quantitative assessmentWe evaluated the performance of different models for the identification of TB using the DecXpert score as a primary predictor (refer to Fig. 2). Initially, Model 1, which relies solely on DecXpert scores for TB detection, demonstrated an area under the receiver operating characteristic (ROC) curve (AUC) of 0.85 (95% CI 0.82–0.87), indicating a reasonably strong predictive ability (refer to Table 3 and Fig. 2). Model 2, which incorporated both the DecXpert score and symptom information, displayed an improved AUC of 0.88 (95% CI 0.83–0.92). When patient demographic information (specifically age and gender) was integrated with DecXpert scores in Model 3, the AUC further increased to 0.91 (95% CI 0.88–0.94) (refer to Table 3 and Fig. 2), indicating an enhanced predictive performance compared to that of earlier models. Furthermore, the development of a composite Model 4, which integrates the DecXpert score, symptom incidence, age, and gender, resulted in a greater AUC of 0.95 (95% CI 0.90–0.97) (refer to Table 3 and Fig. 2).Fig. 2Illustrates the diagnostic accuracy of DecXpert assessed through receiver operating characteristic (ROC) curves generated from the evaluated models in this study. The x-axis denotes different combinations of information used, while the y-axis indicates the corresponding AUC values. The bars represent scenarios including DecXpert scores alone, DecXpert scores combined with symptom information, DecXpert scores combined with age and gender information, and DecXpert scores combined with symptom information, age, and gender. Higher AUC values signify improved discrimination between individuals with and without active tuberculosis. The error bars depict the 95% confidence intervals for each AUC value.Table 3 Summarizes the performance of the DecXpert Computer-Aided Detection system with added patient demographics and clinical information.Performance of the DecXpert algorithm against the gold standard molecular reference GeneXpert MTB/RIFNext, we assessed the performance of the proposed DecXpert model that has proven to be highly effective in the identification of TB cases from CXR images. The GeneXpert MTB/RIF test reports served as the established ground truth for evaluation. The DecXpert model, operating solely on CXR imaging data without incorporating patient symptoms, achieved an AUC of 0.85 (95% CI = 0.82–0.87), indicated by the blue ROC curve in Fig. 3. However, upon inclusion of age and gender, the performance of the DecXpert model improved, yielding an AUC of 0.91 (95% CI = 0.88–0.94), indicated by the green ROC curve in Fig. 3. These results indicate a high level of accuracy ranging between 85 and 91% relative to GeneXpert MTB/RIF test reports, which are considered the gold standard (refer to Fig. 3), suggesting that DecXpert reports could potentially serve as a surrogate for GeneXpert MTB/RIF. Notably, when basic patient demographics, specifically age and gender, and patient symptoms such as cough, fever, hemoptysis and night sweats were omitted, the DecXpert model still demonstrated a strong 85% concordance with GeneXpert MTB/RIF testing.Fig. 3Shows Receiver Operating Characteristic (ROC) curves for the DecXpert Computer-Aided Detection system. The blue curve represents DecXpert scores alone, yielding an Area Under the Curve (AUC) of 0.85 (95% CI 0.82–0.87), while the green curve represents DecXpert scores combined with age and gender information, resulting in an improved AUC of 0.91 (95% CI 0.88–0.94). The x-axis illustrates the false positive rate (1–specificity), while the y-axis depicts the true positive rate (sensitivity). Higher AUC values indicate better overall accuracy in discriminating between individuals with and without tuberculosis. Including age and gender information enhances DecXpert’s diagnostic performance, as evidenced by the higher AUC value for the green curve.Performance of the DecXpert algorithm against 3 board-certified radiologistsAnalysing the overall cohort revealed that the DecXpert algorithm successfully identified 2064 TB patients (88%) out of the total 2345 GeneXpert MTB/RIF-confirmed positive TB patients, whereas the radiologists identified 1665 TB patients (71%). Thus, using the DecXpert algorithm increased the overall TB case detection rate by approximately 1.23 times compared to radiologists. Examining the performance of the three board-certified radiologists as illustrated in Fig. 4, the first radiologist achieved an AUC of 0.79 (95% CI 0.74–0.84), the second radiologist achieved an AUC of 0.72 (95% CI 0.67–0.76), and the third radiologist achieved an AUC of 0.75 (95% CI 0.71–0.78). Notably, each radiologist’s sensitivity/specificity point fell outside the 95% CI space of the DecXpert model’s ROC curve, indicating their performance was inferior to DecXpert (Fig. 4). Furthermore, within the unproductive cough and comorbidity subgroup, there was a considerable improvement in the TB case detection rate, according to DecXpert, which detected 303 patients (71.2%), whereas radiologists were able to detect only 244 patients (57.4%). This observation emphasises the enhanced performance of the DecXpert algorithm compared to that of radiologists, particularly within this subgroup, demonstrating its greater efficiency in identifying TB patients.Fig. 4Diagnostic accuracy of DecXpert and three board certified radiologist experts compared with the reference GeneXpert MTB/RIF. The receiver operating characteristic (ROC) curve illustrates the performance of the DecXpert model (utilising age and gender) alongside the performance metrics of three board-certified radiologist experts, all plotted within the same ROC space. The area under the curve (AUC) quantifies the overall discriminative ability, while CI denotes the confidence interval surrounding the AUC estimation. The asterisk, bubble and square symbols represent the first, second and third radiologists respectively.Qualitative assessmentDecXpert went through a validation process that focused on visualizing CXRs and highlighting specific areas in the image that are important for DecXpert to make decisions when classifying TB cases. The assessment shows DecXpert relies on clinically relevant lung regions from CXRs to make decisions. Figure 5 shows highlighted regions in CXRs of confirmed TB patients from the GeneXpert MTB/RIF test. The model relies on accurate visual information and does not consider misleading visual cues such as symbols, motion artifacts, embedded text or symbols, or imaging irregularities when making decisions. This finding demonstrated that DecXpert-related decision-making behaviour is primarily rooted in clinically relevant features.Fig. 5Visualizing analyzed chest X-rays from sample TB patients. Sample chest X-ray images of TB patients are shown, with the highlighted areas demonstrating the most important aspects detected by the DecXpert system for identifying tuberculosis-related abnormalities.Evaluation of DecXpert algorithm’s suitability for deployment in remote isolated regions with offline and online functionality and minimal hardware needsSubsequently, we evaluated the suitability of integrating DecXpert into the pre-existing CXR workflows within primary healthcare facilities and diagnostic centres, particularly for deployment in geographically isolated regions of the nation where computational hardware capabilities are limited. To this end, we examined both online and offline iterations of the DecXpert software and incorporated them into current TB CXR workflows for the purpose of TB screening and diagnosis at primary healthcare facilities and diagnostic centres. The investigation focused on the implementation of DecXpert across seven distinct providers of digital chest X-ray machines, including GE Healthcareâ„¢, Siemensâ„¢, Philips Healthcareâ„¢, FujiFilm Medical Systemsâ„¢, Shimadzu Corporationâ„¢, Toshiba Medical Systemsâ„¢, and Hitachi Healthcareâ„¢. This examination encompassed varying computational hardware setups, ranging from 500 MB to 16 GB of RAM, and spanning different versions of the Windows operating system (2000, 7, 8, and 10).Additionally, the study investigated the compatibility of DecXpert with all five perspectives of CXR images—posteroanterior (PA), anteroposterior (AP), lateral, decubitus, and oblique views. This assessment was conducted at six remote and geographically dispersed locations within the northern region of India. Moreover, we aimed to ascertain the ease of use of DecXpert by the existing X-ray technicians at these sites within their current CXR workflow. DecXpert demonstrated seamless integration and compatibility with all CXR images from the seven vendors, supporting TB screening and diagnostic workflows. Notably, it functioned effectively with basic computational hardware, such as systems with 500 MB RAM and running Windows 2000. Furthermore, the on-site technicians at these healthcare facilities were easily trained on a simple four-step process for processing CXR images (refer to Fig. 6a–d). DecXpert was available in both offline and online configurations with the same functionality. Figure 6a–d shows the process wherein CXR images and patient demographics are uploaded to DecXpert, which then generates a diagnostic report in PDF format, available electronically or in print for physician assessment.Fig. 6User interface and output displays of the DecXpert software. (a) The DecXpert software login screen. (b) The interface for uploading a chest X-ray image and entering patient details. (c) The output screen displaying the analyzed chest X-ray image along with a color-coded risk assessment for tuberculosis and other potential abnormalities. (d) A portable document format (PDF) report providing an overall risk score and quantitative assessment for TB and 18 other abnormalities, as well as a summary of clinical diagnosis.Cost-effectiveness analysis of using DecXpert as a pre-screening tool for pulmonary TB in resource-limited settingsAfter obtaining encouraging results on the feasibility of integrating DecXpert into pre-existing CXR workflows, we subsequently conducted a cost-effectiveness analysis. This analysis aimed to evaluate the use of DecXpert as a pre-screening tool for patients presenting with symptoms suggestive of pulmonary TB at primary healthcare facilities in resource-limited settings, prior to molecular (GeneXpert MTB/RIF) testing. This strategy was compared to the current diagnostic algorithm for TB case finding and diagnosis, as outlined by the Central TB Division, Government of India17, which involves triaging by a general practitioner (GP) followed by microbiological testing with the GeneXpert MTB/RIF test.The overall cost for a GeneXpert MTB/RIF test in the 145 countries eligible for subsidized GeneXpert MTB/RIF has been estimated at 14.90 USD18 which includes equipment, resources, maintenance and consumables. The cost analysis for the digital radiography system, including equipment and running costs such as labor, maintenance, consumables and depreciation has resulted in a cost of 1.49 USD19,20 per chest X-ray (CXR) screening.Since CXR screening is significantly cheaper and faster than GeneXpert MTB/RIF testing and human reading, we performed a cost-effectiveness analysis of a scenario where DecXpert identifies a proportion of subjects eligible for subsequent GeneXpert MTB/RIF, and the remainder are discharged after the CXR screen (refer to Fig. 7).Fig. 7Two-Stage Screening and Diagnostic Workflow for Tuberculosis Using DecXpert and GeneXpert MTB/RIF. The diagram illustrates the two-stage process for tuberculosis (TB) case finding and diagnosis. In the pre-screening stage, subjects are first subjected to chest X-ray (CXR) and evaluated using DecXpert. Results are obtained within one minute. Subjects with TB positive results on DecXpert are referred for further examination, while those found to be TB negative with DecXpert are not followed up. In the examination stage, subjects undergo GeneXpert MTB/RIF testing, with results available within two hours. Positive results lead to TB treatment, while negative results do not require further follow-up.The DecXpert tool was compared to the current algorithm for TB case finding and diagnosis from the Central TB Division, Government of India, where patients presenting with symptoms suggestive of pulmonary TB at the primary healthcare facility are assessed by a general practitioner (GP). This comparison aimed to illustrate the effect on cost benefits.The following metrics were calculated for both DecXpert and GP in a primary health setting:
Px: The proportion (0–1) of cases that will be sent for subsequent GeneXpert MTB/RIF testing. This includes all cases that would be marked as TB positive, including some false positives.
CostAVG: The average cost for a case arriving at the primary healthcare facility.
\({\text{Cost}}_{{{\text{AVG}}}} = {1}.{49} + \left( {{\text{Px}} \times {14}.{9}0} \right)\)
CostTB: The average cost per TB case detected. This calculation requires an estimate of TB prevalence, for which we used the incidence in the current study: CostTB = CostAVG/TB prevalence
Scenario 1: General Practitioner Screening.A reasonable estimate for the accuracy of primary care physicians in India in identifying tuberculosis (TB) suspects is around 50%21. The study conducted by Satyanarayana et al.21 found that about 50% of presumptive TB cases in the community sought molecular testing, suggesting GPs correctly identify and refer around 50% of true TB suspects.
Px: 1–0.5 = 0.5 (Sensitivity of GP is 0.5 21)
CostAVG: 1.49 + (0.5 × 14.90) = 8.94 USD
CostTB: 8.94/(2345/4495) = 17.14 USD per TB case detected
Scenario 2: DecXpert Screening.
Px: 1–0.88 = 0.12 (Sensitivity of DecXpert is 0.88)
CostAVG: 1.49 + (0.12 × 14.90) = 3.278 USD
CostTB: 3.278/(2345/4495) = 6.29 USD per TB case detected
Cost reductionPercentage decrease in cost per TB case detected = ((General practitioner cost–DecXpert cost)/General practitioner cost) × 100.Percentage decrease in cost per TB case detected = ((17.14–6.29)/17.14) × 100 ≈ 63.32%The results showed that when a general practitioner screens patients for further molecular testing, including GeneXpert MTB/RIF, the average cost per TB case detected was 17.14 USD. In contrast, when DecXpert screens patients for further molecular testing with GeneXpert MTB/RIF, the average cost per TB case detected was 6.29 USD. Therefore, the cost per TB case detected decreases by approximately 63.32% or 2.72 times when using DecXpert screening compared to a GP.These findings suggest that the use of DecXpert as a pre-screening tool for pulmonary TB in resource-limited settings can significantly improve the cost-effectiveness of TB case detection compared to the current standard of care involving a general practitioner. This information can be valuable for decision-makers in resource-limited settings when considering the adoption of DecXpert for improving the efficiency and cost-effectiveness of TB case finding and diagnosis.