AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia

Hemant Ghayvat; Muhammad Awais; A K Bashir; Sharnil Pandya; Mohd Zuhair; Mamoon Rashid; Jamel Nebhen

doi:10.1007/s00521-022-07055-1

AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia

Neural Comput Appl. 2023;35(20):14591-14609. doi: 10.1007/s00521-022-07055-1. Epub 2022 Mar 1.

Authors

Hemant Ghayvat^{1

2

3}, Muhammad Awais⁴, A K Bashir^{5

6}, Sharnil Pandya⁷, Mohd Zuhair⁸, Mamoon Rashid⁹, Jamel Nebhen¹⁰

Affiliations

¹ Innovation Division, Technical University of Denmark, Lyngby, Denmark.
² Department of Computer Science and Media Technology, E-health Unit (Improved Data to and from Patients), Linnaeus University, Vaxjo, Sweden.
³ Building Realization and Robotics, Technical University of Munich, Munich, Germany.
⁴ Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433 China.
⁵ Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.
⁶ School of Information and Communication Engineering, University of Electronics Science and Technology of China (UESTC), Chengdu, China.
⁷ Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharashtra India.
⁸ Department of Computer Science and Engineering, Nirma University, Ahmedabad, Gujarat India.
⁹ Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India.
¹⁰ College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia.

Abstract

A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.

Keywords: Artificial intelligence; COVID-19, pneumonia; CT; Diagnosis system; Medical image processing; Radiologist; X-ray.