Tuberculosis (TB) is the deadliest infectious bacteriological disease in the world. The World Health Organization (WHO) Global Report revealed that TB infection was responsible for the death of 1.25 million people in 2023 alone, which is despite available vaccine and cure for TB [1]. This report showed that annual TB estimates by far exceed reported cases by 3.5 million, thereby creating a diagnostic gap mostly thought to be in target populations probably not reached by the campaigns [1]. Effort has shifted focus from TB control to TB elimination not minding the drawbacks from COVID-19 pandemic. This means that, for this global dream to be achieved, there must be innovative ways, in addition to the less sensitive symptom screening (W4SS), to increase TB case detection in order to close the existing diagnostic gap and prevent further gaps. Owing to this, Chest X-Ray (CXR) with Computer-Aided Detection (CAD) has become of great relevance in this pursuit [2].

In Nigeria, diagnostic coverage for TB case finding still stands at 40% for GeneXpert and more gaps still exist for AI-based diagnostic tools [3]. Chest X-ray with AI has been recommended by WHO both for TB screening and diagnosis. In the country, the first portable digital X-ray (PDX) with CAD4TB was introduced and piloted by KNCV Nigeria through the TB LON project since December 2020 [4]. This was necessitated by the need to reach people in hard-to-reach remote communities. This pilot led to the scale up of the intervention under the USAID new tools project with additional seven PDX distributed to other states (Benue, Cross River, Delta, Katsina, Kano and Nasarawa) under the KNCV intervention [4]. Uptake of this technology has been shown to have the greatest yield in TB diagnosis; however, the use has been slow within the NTBLCP. Presently only 12 units are in operation for TB case finding across nine states in the Nigeria despite demonstrated high yield in the KNCV pilot intervention in selected hard-to-reach locations in Nigeria.

Chest X-ray, being a highly sensitive TB screening tool for detecting people who require diagnostic evaluation, may guide the effective allocation of diagnostic tests (GeneXpert, Truenat, TB lamp, LF Lam) to improve case detection and cost-efficiency [5]. CXR also helps in detection of asymptomatic sub-clinical TB as such leading to early detection, initiation to treatment and halting of transmission [6][7]. Computer-Aided Detection (CAD) software products are increasingly being used as a tool to enhance the feasibility and accuracy of CXR interpretation [8][9]. The use of Artificial Intelligence (AI) to analyze CXR for abnormalities suggestive of pulmonary TB provides an abnormality score that can be used to determine the need for follow-up on diagnostic testing for TB relative to a selected threshold. Such a technology can be used to replace or augment human expert interpretation of plain CXR when screening for pulmonary TB. Computer-aided detection (CAD) can be utilized with CXR to evade CXR’s high inter- and intra-reader variability, long turnaround times and the lack of radiologists in high burden countries [10]-[13]. CAD technologies for TB detection were considered by the WHO in 2020 as part of an update to TB screening guidelines and recommendations [14]. However, there is an inherent trade-off in the selection of the threshold score; a lower threshold score will maximize sensitivity of the tool to detect true TB cases among the population being screened but will incur additional costs related to unnecessary follow-on diagnostic testing due to reduced specificity. On the other hand, a higher threshold score will reduce the volume, and thus costs, of follow-on diagnostic testing and will likely identify more severe cases, but its reduced sensitivity will result in missed cases. This buttresses the need for local calibration of CAD [15].

Currently, no official guidelines exist for selecting a decision threshold. Also, most CAD software products do not come with a pre-set manufacturer-recommended threshold that defines cut-off to be referred for diagnostic evaluation for TB. Even when some manufacturers recommend thresholds for defining images as abnormal, evaluations by the WHO Guideline development group demonstrated substantial variation in the diagnostic accuracy (sensitivity and specificity) of CAD programmes across settings, even within the same technology set to the same threshold. This calls for calibration of CAD thresholds for a given software based on setting and population in which it will be used [15]. Several studies also suggested the importance of obtaining a study-specific decision threshold, adjusted to the underlying TB prevalence and demographic parameters [2][16]. One way to select a threshold score is by conducting on-site calibration studies [17]. This study, therefore, seeks to calibrate CAD4TB to local settings and population groups by comparing the diagnostic accuracy of CAD compared to a reference standard as this has the potential to increase active TB case finding and advance the goal of eradicating TB by 2030.

Table 1. Sociodemographic characteristics and setting of participants.

Table 1 shows the sociodemographic characteristics and settings of participants. Higher proportion of residents were in general population or normal home setting 5620 (35.0%) followed by those in slums 4058 (25.3%) then hard to reach areas 2644 (16.5%). The mean age of participants was 43.45 years. those aged 25 to 34 years were slightly higher in proportion 3505 (20.0%). Males were more than Females 62.6% versus 37.4%. About 802 (5.0%) were previously treated for TB. In all 1752 (10.9%) have TB disease. The Mean CAD4 score for participants was 43.13 with a range of scores O to 99.3.

Table 2. Comparison of mean CAD4 scores based on characteristics of participants.

Table 2 shows Comparison of Mean CAD4 scores based on characteristics of participants. Those located at health facility have highest mean 55.05 followed by those in slums 49.53 then those at correctional centres 47.65 with the least among the general population. There was a statistically significant difference in mean CAD4 score among them p < 0.001. Based on age there were similar mean scores (between 41.0 and 49.0). There was equally a statistically significant difference in mean CAD4 score among them p < 0.001. For gender, the mean values were 46.05 and 44.96 for males and females respectively. There was no statistically significant difference in mean CAD4 score among them p = 0.118. The mean value was higher for those previously treated for TB compared to those not previously treated (48.38 versus 45.32). There was equally a statistically significant difference in mean CAD4 score among them p = 0.004. Also, the mean value was higher for those with TB compared to those without TB (67.92 versus 40.10). There was equally significant difference in the mean CAD4 score among them p < 0.001.

Table 3.Distribution of AUC, sensitivity, specificity and CAD4 score of overall participants.

*Sen—Sensitivity *Spec—Specificity.

Table 3 shows Overall AUC of participants. The AUC was 0.810 with confidence Interval of 0.799 to 0.822. The CAD4 threshold cut-off is 37 (Sen 90.20%, Spec 39.50%) considering the missed cases of 13 versus and True Positive (TP) of 116, against missed cases of 25 and TP of 104 (Sen 80.70%, Spec 69.70%).

Figure 1. Plot showing overall AUC for all participants.

Figure 1 shows that AUC was 0.810.

Table 4. CAD4 cut off for participants.

Table 4shows that at a sensitivity of 90.20 % the corresponding specificity was 39.50% and CAD4TB threshold score of 37. At this score missed TB cases was 13 (9.80%), True Positive (TP) 116 and attracted about 1,529,856 dollars for confirmatory test. Likewise, at Specificity of about 69.70% the corresponding sensitivity was 80.70% and CAD4TB score was 49. At this score missed TB cases was 25 (19.30%), TP 104 and attracted about 860,273 dollars for confirmatory test.

The WHO recommends that CAD can replace a human reader of Xray in TB investigation. This expectedly will be in two broad contexts; for screening and for triage with both aims to establish whether an individual should receive confirmatory diagnostic tests. CAD4TB produces an abnormality score that can be used to signal probable TB and trigger further TB diagnostic evaluation relative to a selected threshold [7]. However, effective integration of CAD4TB into routine programming requires the calibration of CAD products. This calibration is based on the local context, intended use as well as decision making around the goals for screening and acceptable costs [15]. Based on this, the WHO sets the target accuracy for TB triage tests at ≥90% sensitivity and ≥70% specificity.

The current study reported that the mean CAD4 score for participants was 43.13 with a range of scores 0 to 99. Overall, at a sensitivity of about 90% and corresponding specificity of 40% (sen. 90.20% Versus spec 39.50%) the CAD4TB threshold score was 37 which was chosen as the acceptable cut off for general population. At this cut off missed TB cases was 13 (9.80%), True Positive (TP) 116 and attracted about 1,529,856 dollars for confirmatory test. Conversely, at specificity of about 70% CAD4TB and corresponding sensitivity of about 80% (sen. 80.70% versus spec. 69.70%) CAD4TB score was 49. At this cut off missed TB cases was 25 (19.30%), TP of 104 and attracted about 860,273 dollars for confirmatory test. This chosen cut-off is not in line with WHO’s minimum target accuracy for TB triage tests which is ≥90% sensitivity and ≥70% specificity. Not minding, this score demonstrated high accuracy and meets the desired standard value for use in screening and triaging for TB. Owing that this is a screening test with aim to maximize case detection, high sensitivity is more important than high specificity. The disparity in missed cases is enormous to ignore, bearing in mind that a person with active pulmonary tuberculosis (TB) can potentially infect a significant number of people, with estimates ranging from 10 to 15 individuals per year if left untreated. This implies 120 to 180 people are at risk based on the difference alone. The chosen threshold was guided by the main goals of the program, balancing reduction in confirmatory testing with risk of missed cases, and considerations of cost-effectiveness [18]-[20].

This study findings are good and underscore the brilliant performance of CAD4TB. It demonstrates its potential to enhance TB triage, particularly in resource-limited settings in identification of TB cases, support timely treatment and reduce TB transmission. This finding has a far-reaching implication. It can help support programmatic decision-making in the implementation of CAD Algorithms. The thresholds identified may be useful as a starting point, although updated versions of CAD algorithms may require re-assessment [18][19]. A study that compared versions of CAD4TB software reported that no threshold from any version was able to match the performance target by the WHO of ≥90% sensitivity and ≥70% specificity. The study therefore identified one threshold for each CAD4TB version that most closely matched the radiologist sensitivity at 80.8% lower specificity than the radiologist. The selected threshold ranged from 20 to 47 [15][21].

At this cut-off from the current study, the Area under Curve (AUC) from Receiver Operating Characteristic plot was 0.810 which is good. Receiver Operating Characteristic (ROC) curves measure the overall performance of the CAD software; however in choosing an acceptable cut off for CAD4 TB, there is an inherent trade-off in the selection of the threshold score; a lower threshold score will maximize sensitivity of the tool to detect true TB cases among the population being screened but will incur additional costs related to unnecessary follow-on diagnostic testing due to reduced specificity. On the other hand, a higher threshold score will reduce the volume, and thus costs, of follow-on diagnostic testing and will likely identify more severe cases, but its reduced sensitivity will result in missed cases. This buttresses the need for local calibration of CAD [15].

A study that determined CAD4 thresholds through secondary data analysis of Lesotho national tuberculosis prevalence survey a CAD4TBv7 threshold chose cut-off 13 at a sensitivity of 89.7% (95% CI 84.6 - 94.8) and a specificity of 74.2% (73.6 - 74.9), close to World Health Organization (WHO) target product profile criteria [21]. The difference in cut off from current study may be due to study setting and design. While the previous study was secondary data which is prone to missing data and assumptions, the current data was a validated primary data that addressed the shortcomings of study in Lesotho. The previous study also used multiple imputations to estimate tuberculosis status for those eligible and in that study, those not tested as well as those assumed not eligible for testing were categorized as negative which is not the case with current study.

Another study on accuracy of computer-aided chest X-ray screening in the Kenya National Tuberculosis prevalence survey indicated that accuracy from their test was non-inferior to WHO-recommended minimum values; CAD4TBv6 was reported to have sensitivity of 0.93 [0.90 - 0.96] p < 0.0001, specificity 0.69 [0.67 - 0.71], p = 0.0003) [22]. This study used a fixed cut-off probably from manufacturers and comparison was against gold standard while current study determined a tentative cut-off for the country. Equally, a previous study on iterative evaluation of mobile computer assisted digital chest X-ray screening for TB in Nigeria reported use of CAD scores ≥80 for persons initially spot -sputum negative and found 11 additional TB cases (6.3%) after 121 person-years of follow-up. It further documented that high CAD scores can identify subclinical TB and those at risk of progression to bacteriologically confirmed TB disease in the near term [23]. However, these identified cases may be due to latent infection, re-infection or newly infected bearing in mind the mode of transmission. Also, the study was not designed to calibrate and identify CAD threshold for cut-off. This most likely accounts for difference with the current study which showed decrease in True positives and increased in missed cases.

The strength of this study is that it included participants with cough and those who did not have cough and met other screening criteria for TB testing. This provides more generalizable results and powered the study to assess threshold identification by both countries and subgroups. The thresholds identified from this study may be useful as a starting point, although updated versions of CAD algorithms may require re-assessment which is a limitation.

Findings suggest that CAD systems could be a useful tool for TB screening programs in high throughput programmes such as active case-finding interventions where a quick decision on further testing is critical, especially if trained readers are scarce and/or costs are high. It provides valuable insights into calibration of CAD4TB for the entire population and subpopulations or settings, offering a robust framework for its deployment in diverse settings. CAD algorithms can achieve or exceed the minimum target accuracy for a TB triage test, with improvement when using setting- or population-specific thresholds. Continuous refinement of CAD4TB, based on real-world data, is essential to improving their accuracy and reliability. Integrating CAD4TB into routine TB detection programs can help bridge gaps in TB case detection, leading to better health outcomes globally.