Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs
Seowoo Lee, Jae‐Joon Yim, Nakwon Kwak, Yeon Joo Lee, Jung‐Kyu Lee, Ji Yeon Lee, Ju Sang Kim, Young Ae Kang, Doosoo Jeon, Myoung‐jin Jang, Jin Mo Goo, Soon Ho Yoon
Abstract
< .001) and decreased during treatment (baseline, 3 months, and 6 months: 0.85, 0.51, and 0.26, respectively). Conclusion A deep learning model performed similarly to radiologists for accurately determining the activity of pulmonary tuberculosis on chest radiographs; it also was able to follow posttreatment changes. © RSNA, 2021
Topics & Concepts
MedicineRadiographyReceiver operating characteristicPulmonologistCohortTuberculosisPulmonary tuberculosisNuclear medicineRadiologySurgeryInternal medicinePathologyIntensive care medicineCOVID-19 diagnosis using AITuberculosis Research and EpidemiologyLung Cancer Diagnosis and Treatment