ArticleNeural computing & applications2023
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.
Article in Neural computing & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- External validation of SpineNetV2 on a comprehensive set of radiological features for grading lumbosacral disc pathologies.North American Spine Society journal · 2024Article
- COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.Sensors (Basel, Switzerland) · 2024Review
- Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays.Diagnostics (Basel, Switzerland) · 2023Article
- An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique.Journal of healthcare engineering · 2023Article
- Early Detection and Diagnosis of Chronic Kidney Disease Based on Selected Predominant Features.Journal of healthcare engineering · 2023Article
- Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Hypertuned Deep Convolutional Neural Network for Sign Language Recognition.Computational intelligence and neuroscience · 2022Article
- An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors at 7 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.