Deep learning for detecting pulmonary tuberculosis via chest
radiography: an international study across 10 countries
Shahar Jamshy Neeral Beladia Katherine Chou Yun Liu Greg S. Corrado Sahar Kazemzadeh Krish Eswaran Atilla Kiraly Shruthi Prabhakara Sreenivasa Raju Kalidindi Jameson Malemela Po-Hsuan Cameron Chen Thad Hughes Charles Lau Christina Chen Jin Yu Ting Shih Rory Pilgrim Monde Muyoyeta Scott Mayer McKinney Lily Peng Daniel Tse Zaid Nabulsi Shravya Shetty
作者信息
Abstract
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO
recommends chest radiographs (CXRs) for TB screening, the limited availability
of CXR interpretation is a barrier. We trained a deep learning system (DLS) to
detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and
Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy
student semi-supervised learning. Evaluation was on (1) a combined test set
spanning China, India, US, and Zambia, and (2) an independent mining population
in South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the
DLS's operating point was prespecified to favor sensitivity over specificity.
On the combined test set, the DLS's ROC curve was above all 9 India-based
radiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity
(88%) was higher than the India-based radiologists (75% mean sensitivity),
p<0.001 for superiority; and its specificity (79%) was non-inferior to the
radiologists (84% mean specificity), p=0.004. Similar trends were observed
within HIV positive and sputum smear positive sub-groups, and in the South
Africa test set. We found that 5 US-based radiologists (where TB isn't endemic)
were more sensitive and less specific than the India-based radiologists (where
TB is endemic). The DLS also remained non-inferior to the US-based
radiologists. In simulations, using the DLS as a prioritization tool for
confirmatory testing reduced the cost per positive case detected by 40-80%
compared to using confirmatory testing alone. To conclude, our DLS generalized
to 5 countries, and merits prospective evaluation to assist cost-effective
screening efforts in radiologist-limited settings. Operating point flexibility
may permit customization of the DLS to account for site-specific factors such
as TB prevalence, demographics, clinical resources, and customary practice
patterns.引用本文复制引用
Shahar Jamshy,Neeral Beladia,Katherine Chou,Yun Liu,Greg S. Corrado,Sahar Kazemzadeh,Krish Eswaran,Atilla Kiraly,Shruthi Prabhakara,Sreenivasa Raju Kalidindi,Jameson Malemela,Po-Hsuan Cameron Chen,Thad Hughes,Charles Lau,Christina Chen,Jin Yu,Ting Shih,Rory Pilgrim,Monde Muyoyeta,Scott Mayer McKinney,Lily Peng,Daniel Tse,Zaid Nabulsi,Shravya Shetty.Deep learning for detecting pulmonary tuberculosis via chest
radiography: an international study across 10 countries[EB/OL].(2021-05-16)[2026-04-30].https://arxiv.org/abs/2105.07540.学科分类
医学研究方法/临床医学/基础医学
评论