Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

Abstract

Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.

Evaluation of an artificial intelligence (AI) system to detect tuberculosis on chest X-ray at a pilot active screening project in Guangdong, China in 2019

“…The AI-based CAD system employed in this active screening pilot achieved better AUCs than onsite and senior radiologists when taking the results of final clinical and bacteriological result as the ground truth. This result is consistence with results reported by Cao XF, et al, [17]. In our study, with sensitivity of 90%, the AI-based CAD system shows specificities larger than 70%, which meets the WHO-recommended minimum values (90% for sensitivity, 70% for specificity).…”

“…The two screening sites had a total population of 3420, and therefore, the rough TB prevalence was 234/100,000, with rough incidence 205/100,000,which is not only higher than the estimated incidence rate of 58/100,000 in China, but also higher than the notification rate 65/100,000 in Guangdong Province in 2019 [13]. Unlike other retrospective studies [9,17,18], our study is one of the few studies perform prospectively screening on healthy population. The AI-based CAD system employed in this active screening pilot achieved better AUCs than onsite and senior radiologists when taking the results of final clinical and bacteriological result as the ground truth.…”

“…Demographic characteristics of participants in the active TB screening pilot project, Yingde, Guangdong, December[16][17][18][19][20][21][22][23][24][25][26] 2019 …”

Diagnostic Modalities of Tuberculosis- Then and Now

“…(g) Artificial Intelligence (AI): AI is the area of computer science that helps in development of tools that can mimic human like thought processing, reasoning and self -correction abilities. A computer-aided detection system is the need of day for screening and diagnosis of TB and other lung diseases using chest x-rays [70]. It will help to diminish the human error in result interpretation as well as workload on pathologists.…”

Lung cancer medical images classification using hybrid CNN-SVM

“…It is difficult to differentiate cancerous nodules from other pulmonary nodules that are not cancerous by only depending on visual estimation, which is a traditional way for healthcare workers to examine CT scans. Computer-Aided Detection (CAD) systems have been utilized to process medical images such as CT scan images to assist the medical personnel in the early detection of lung cancer [11]- [14]. It has been reported that CAD systems have an enormous positive impact on the job of radiologists in detecting cancer, and the systems could be used as a second reader [15].…”