The objectives of this study were to assess the tuberculosis (TB) burden and to provide an insight into the type of circulating M. tuberculosis species in urban slums of Bangladesh. We also aimed to test the feasibility of a larger transmission study in this setting. This cross-sectional study was conducted in an urban slum of Dhaka city. The household members were actively screened to assess the presence of TB-related signs and symptoms; cough ≥3 weeks and body mass index (BMI) <17 kg/m². Sputum specimens from suspects were collected for acid fast bacilli (AFB) microscopy, culture and drug susceptibility testing. Genotyping of M. tuberculosis was done using spoligotyping and variable number tandem repeats of mycobacterial interspersed repetitive units typing. Among 9,877 adult screened for pulmonary TB (PTB), 25 were positive for AFB on microscopy and/or culture and the prevalence of new PTB cases was estimated to be 253/100,000. Only one child TB case was diagnosed among 5,147 child screened. Out of 26 cases, 21(81%) had cough for several duration and 5(19%) did not present with cough at the time of screening. One multidrug resistant case was found. Fifty two percent of all TB cases had BMI <17 kg/m2 (p = <0.001). Among the 20 analyzed isolates, 13 different spoligotype patterns were identified in which 5 clusters contained 12 strains and 8 strains had unique pattern. The study revealed high prevalence of TB in urban slums. Screening using low BMI can be beneficial among risk group population. It is important to conduct larger study to validate clinical variables like cough <3 weeks and low BMI to define TB suspect and also to investigate the transmission of TB in slum settings.

Computer-aided reading (CAR) of medical images is becoming increasingly common, but few studies exist for CAR in tuberculosis (TB). We designed a prospective study evaluating CAR for chest radiography (CXR) as a triage tool before Xpert MTB/RIF (Xpert).Consecutively enrolled adults in Dhaka, Bangladesh, with TB symptoms received CXR and Xpert. Each image was scored by CAR and graded by a radiologist. We compared CAR with the radiologist for sensitivity and specificity, area under the receiver operating characteristic curve (AUC), and calculated the potential Xpert tests saved.A total of 18 036 individuals were enrolled. TB prevalence by Xpert was 15%. The radiologist graded 49% of CXRs as abnormal, resulting in 91% sensitivity and 58% specificity. At a similar sensitivity, CAR had a lower specificity (41%), saving fewer (36%) Xpert tests. The AUC for CAR was 0.74 (95% CI 0.73–0.75). CAR performance declined with increasing age. The radiologist grading was superior across all sub-analyses.Using CAR can save Xpert tests, but the radiologist's specificity was superior. Differentiated CAR thresholds may be required for different populations. Access to, and costs of, human readers must be considered when deciding to use CAR software. More studies are needed to evaluate CAR using different screening approaches.

Background: Sputum smear microscopy is fast and inexpensive technique for detecting tuberculosis (TB) in high incidence areas but has low sensitivity. Physical and chemical sputum processing along with centrifugation have been found to show promise in overcoming this limitation. Our objective was to compare the sensitivity of smear microscopy obtained with smears made directly from respiratory specimens to those from concentrated specimens. Methods: By active screening, 915 TB suspects were identified from Dhaka Central Jail and sputum specimens were aseptically collected. Direct smears were prepared by taking a small portion of the purulent part of the sputum with a sterile loop. The specimens were then processed by a standard N-acetyl-L-cysteine-NaOH digestion-decontamination method to prepare concentrated specimens. Both smears were then air dried, heat fixed, and stained by the Ziehl-Neelsen staining technique. The stained slides were examined under oil immersion and were graded following International Union Against Tuberculosis and Lung Diseases guidelines. All the specimens were inoculated into Lowenstein-Jensen (L-J) media and culture results were considered as gold standard to calculate sensitivity. Results: Of 915 specimens, 73 (8%) specimens were positive both on direct and concentrated methods, one sample was positive on direct microscopy but was negative on concentrated method. An extra 14 (1.5%) samples were positive on concentrated method which were negative on direct smear. In L-J media 105 specimens were found positive for TB bacilli and of them, 74 (70.5%) and 87 (82.9%) were positive in direct and concentrated smear, respectively. The sensitivity of direct and concentrated smear microscopy was different when using positive culture as the gold standard (71% vs. 83%). Conclusions: The results showed that concentrated technique increases the sensitivity of microscopy up to 12%. Therefore, the national programs in high TB burden countries may consider incorporating the technique into their guidelines at least in the district and higher level laboratories to improve case finding strategy.

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.

Background: 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. Methods: 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. Results: 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%. Conclusions: 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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