# Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

arXiv: https://arxiv.org/abs/2003.05037 · HTML: https://mlsanity.com/paper/2003.05037/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 30 of 38)

- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [An Explainable Non-local Network for COVID-19 Diagnosis](https://mlsanity.com/paper/2408.04300/) (2024)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [Quadruple Augmented Pyramid Network for Multi-class COVID-19 Segmentation via CT](https://mlsanity.com/paper/2103.05546/) (2021)
- [Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network](https://mlsanity.com/paper/2104.02060/) (2021)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data](https://mlsanity.com/paper/2205.07682/) (2022)
- [Performance of GAN-based augmentation for deep learning COVID-19 image classification](https://mlsanity.com/paper/2304.09067/) (2023)
- [A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis](https://mlsanity.com/paper/2101.05410/) (2021)
- [An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease](https://mlsanity.com/paper/2104.14116/) (2021)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [CxSE: Chest X-ray Slow Encoding CNN forCOVID-19 Diagnosis](https://mlsanity.com/paper/2106.12157/) (2021)
- [Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought](https://mlsanity.com/paper/2108.05649/) (2021)
- [COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification](https://mlsanity.com/paper/2312.12876/) (2023)
- [Explainability Guided Multi-Site COVID-19 CT Classification](https://mlsanity.com/paper/2103.13677/) (2021)
- [DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2105.09683/) (2021)
- [CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19](https://mlsanity.com/paper/2106.05861/) (2021)
- [Understanding Human Innate Immune System Dependencies using Graph Neural Networks](https://mlsanity.com/paper/2108.02872/) (2021)
- [Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database](https://mlsanity.com/paper/2109.09001/) (2021)
- [AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients](https://mlsanity.com/paper/2110.15558/) (2021)
- [Detecting COVID-19 from Chest Computed Tomography Scans using AI-Driven Android Application](https://mlsanity.com/paper/2111.06254/) (2021)
- [Machine learning approaches for localized lockdown during COVID-19: a case study analysis](https://mlsanity.com/paper/2201.00715/) (2022)
- [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022)
- [Prediction of COVID-19 using chest X-ray images](https://mlsanity.com/paper/2204.03849/) (2022)
- [Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image](https://mlsanity.com/paper/2206.00002/) (2022)

## Abstract (arXiv)

Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.