Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs
Abstract Traditional tuberculosis (TB) screening involves radiologists manually reviewing chest X-rays (CXR), which is time-consuming, error-prone, and limited by workforce shortages. Our AI model, AIRIS-TB (AI Radiology In Screening TB), aims to address these challenges by automating the reporting...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01832-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849331558872252416 |
|---|---|
| author | Prateek Munjal Ahmed Al Mahrooqi Ronnie Rajan Andrew Jeremijenko Iftikhar Ahmad Muhammad Imran Akhtar Marco A. F. Pimentel Shadab Khan |
| author_facet | Prateek Munjal Ahmed Al Mahrooqi Ronnie Rajan Andrew Jeremijenko Iftikhar Ahmad Muhammad Imran Akhtar Marco A. F. Pimentel Shadab Khan |
| author_sort | Prateek Munjal |
| collection | DOAJ |
| description | Abstract Traditional tuberculosis (TB) screening involves radiologists manually reviewing chest X-rays (CXR), which is time-consuming, error-prone, and limited by workforce shortages. Our AI model, AIRIS-TB (AI Radiology In Screening TB), aims to address these challenges by automating the reporting of all X-rays without any findings. AIRIS-TB was evaluated on over one million CXRs, achieving an AUC of 98.51% and overall false negative rate (FNR) of 1.57%, outperforming radiologists (1.85%) while maintaining a 0% TB-FNR. By selectively deferring only cases with findings to radiologists, the model has the potential to automate up to 80% of routine CXR reporting. Subgroup analysis revealed insignificant performance disparities across age, sex, HIV status, and region of origin, with sputum tests for suspected TB showing a strong correlation with model predictions. This large-scale validation demonstrates AIRIS-TB’s safety and efficiency in high-volume TB screening programs, reducing radiologist workload without compromising diagnostic accuracy. |
| format | Article |
| id | doaj-art-682fbeb040f34c97a109d60cd755a21b |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-682fbeb040f34c97a109d60cd755a21b2025-08-20T03:46:29ZengNature Portfolionpj Digital Medicine2398-63522025-07-01811810.1038/s41746-025-01832-7Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRsPrateek Munjal0Ahmed Al Mahrooqi1Ronnie Rajan2Andrew Jeremijenko3Iftikhar Ahmad4Muhammad Imran Akhtar5Marco A. F. Pimentel6Shadab Khan7M42M42M42Capital Health Screening CentreCapital Health Screening CentreCapital Health Screening CentreM42M42Abstract Traditional tuberculosis (TB) screening involves radiologists manually reviewing chest X-rays (CXR), which is time-consuming, error-prone, and limited by workforce shortages. Our AI model, AIRIS-TB (AI Radiology In Screening TB), aims to address these challenges by automating the reporting of all X-rays without any findings. AIRIS-TB was evaluated on over one million CXRs, achieving an AUC of 98.51% and overall false negative rate (FNR) of 1.57%, outperforming radiologists (1.85%) while maintaining a 0% TB-FNR. By selectively deferring only cases with findings to radiologists, the model has the potential to automate up to 80% of routine CXR reporting. Subgroup analysis revealed insignificant performance disparities across age, sex, HIV status, and region of origin, with sputum tests for suspected TB showing a strong correlation with model predictions. This large-scale validation demonstrates AIRIS-TB’s safety and efficiency in high-volume TB screening programs, reducing radiologist workload without compromising diagnostic accuracy.https://doi.org/10.1038/s41746-025-01832-7 |
| spellingShingle | Prateek Munjal Ahmed Al Mahrooqi Ronnie Rajan Andrew Jeremijenko Iftikhar Ahmad Muhammad Imran Akhtar Marco A. F. Pimentel Shadab Khan Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs npj Digital Medicine |
| title | Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs |
| title_full | Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs |
| title_fullStr | Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs |
| title_full_unstemmed | Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs |
| title_short | Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs |
| title_sort | population scale cross sectional observational study for ai powered tb screening on one million cxrs |
| url | https://doi.org/10.1038/s41746-025-01832-7 |
| work_keys_str_mv | AT prateekmunjal populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT ahmedalmahrooqi populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT ronnierajan populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT andrewjeremijenko populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT iftikharahmad populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT muhammadimranakhtar populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT marcoafpimentel populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs AT shadabkhan populationscalecrosssectionalobservationalstudyforaipoweredtbscreeningononemillioncxrs |