Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images

Abstract

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

“…This approach also has value as a preliminary screening tool aiming to diminish the workload on hospital staff and reduce the rate of misdiagnosis of patients with COVID-19 [178][179][180]. The enhanced prediction of disease severity via AI on CT images allows improved mortality prediction [126,129,[181][182][183][184][185][186][187] and discrimination from other forms of pneumonia not due to SARS-CoV-2 [188][189][190]. Some works [79,117] created a radiomicsand DL-based model showing the robustness of the approach on data from several sites.…”

Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning

“…Innumerous studies exist on the retrieval of visually and semantically relevant CXRs [ 1 , 3 , 4 , 31 , 32 , 33 ]. Similar approaches have also been developed for retrieval of images along with associated clinical records in COVID-19 patients [ 34 , 35 , 36 , 37 , 38 ]. Though case retrieval has been extensively studied as computer aided diagnosis methods, the potential of relevant case analysis for progression and prognosis prediction in COVID-19 patients requires extensive investigation.…”

A Comprehensive Review of Machine Learning Used to Combat COVID-19

“…Infection localization was also analyzed and precision of 84.8% with a recall of 75.9% was reported. This study [ 149 ] focused on the development of a deep learning model that can extract features from CT imaging in COVID-19 patients and assess disease severity. The dataset was from multiple sources that consisted of 349 CT images from 216 COVID-19 positive patients and 397 COVID-19 negative CT images.…”