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RAPID-CT project for CT scan triage and segmentation algorithm of lung mask detection
diagnosis on 100 chest X-rays as test set, from the pool of
RAPID-CT aims to reduce turnaround time for 1000 X-rays from RSNA dataset. We obtained
triage of medical images while also prioritization 0.91 as Jaccard similarity index on the validation
of patients based on their condition in a clinical set. We validated the lung opacity detection
setting automatically. As a case study, module on 1012 test X-ray images from RSNA
Computed Tomography scans of the Head for Kaggle dataset. The exactness of object
detection and diagnosis of Intracranial detection is usually well determined by mAP
Hemorrhages (ICH) has been selected as an (Mean Average Precision), for opacity detection
initial challenge. RAPID-CT also aims to provide mAP of 0.34 is achieved. CovBaseAI algorithm
a software for assessment of CT scans in a was found to have an accuracy of 87% with
remote setting. Apart from patient samples negative predictive value of 98% in the
available publicly from RSNA to be used for quarantine-center data for Cov-Pneum.
training, we collected patient samples from a However, sensitivity varied from 0.66 to 0.90
radiology clinic as a test set. We then built depending on whether RT-PCR or the
modules for anonymization and standardization radiologist’s opinion was set as ground truth.
of CT, and for collection and inference of the Since the CovBaseAI initiative several new
models which we then integrated into a web datasets for SARS-CoV detection are publicly
portal. The models we built for detection of ICH available which needs to be incorporated in the
presence and subtypes, and localization of ICH algorithm. Further, there are plans to work on
on the CT slices had an accuracy of 95.6% and comparing the efficacy of CXRs as compared to
98% for ICH detection respectively. In the future CT scans for detection of COVID-19 and to build
we plan to work on the following avenues: (i) a better version of the model.
Build better models for ICH detection, diagnosis
and localization including patient level Brain tissue-specific proteoforms
detection models. (ii) Work on generalizability The human brain is a complex network of
of models in multiple hospitals in a clinical structural and functional systems. Its
setting, and (iii) Translate skills and toolsets complexity is majorly governed by the
developed for ICH into other use cases in the expressed proteins. Alterations or mutations at
near future. different levels from genome to proteome give
rise to various proteoforms. Various studies
CovBaseAI–COVID 19 detection and diagnosis have shown that proteoforms not only show
CovBaseAI project aims to develop an AI distinctive tissue specificity but also lead to
classifier for detection and diagnosis of COVID- variability in phenotypic traits. Diversity in
Pneumonia from Chest X-Rays (CXRs). The AI peptides that gives rise to different proteoforms
classifier predicts Covid-Pneumomia and is is important in several neurological disorders
composed of an ensemble model consisting of like Alzheimer’s disease. The proteoforms
three DL modules feeding to an expert decision cannot be directly predicted from the genome
system. The DL modules as part of the ensemble but using proteogenomics for integrating
include pathology classification, lung genome/transcriptome with proteomics data
segmentation, and opacity detection models can reveal deeper insights into the identification
and are explainable to the extent of an and tissue-specific expression of such novel
activation map output. The expert decision proteoforms. Thus, a detailed proteogenomic
system is a rule-based classification system that study analyzing proteins across different tissues
classifies the X-ray into one of three classes, of the human brain can provide information on
namely COVID-unlikely, indeterminate, and how proteoforms relate to human biology and
COVID-likely and is fully explainable as well as disease. We conducted deep proteogenomic
modifiable as needed. We validated the profiling of publicly available proteomics data
Annual Report 2020-21 52
