PhD Scientific Days 2022

Clinical Medicine VI. (Poster discussion will take place on the terrace of the room during the Coffee Break)

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia

Előadó neve

Szabó, István Viktor

Előadó munkahelye

Semmelweis University Medical Imaging Centre

Előadó telefonszáma

+36202839347

Előadó e-mail címe

istvan.szabo97@gmail.com

Az előadás címe

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia

Szerző(k) neve és munkahelye

István Viktor Szabó 1, Judit Simon 1,2, Chiara Nardocci 1, Anna Sára Kardos 1,2, Norbert Nagy 1, Renad-Heyam Abdelrahman 1, Emese Zsarnóczay 1,2, Bence Fejér 1, Balázs Futácsi 1, Veronika Müller 3, Béla Merkely 2, Pál Maurovich-Horvat 1,2

1 Medical Imaging Centre, Semmelweis University
2 MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University
3 Department of Pulmonology, Semmelweis University

Bemutatás módja

Szóbeli

Szekció

Clinical Medicine VI. (Poster discussion will take place on the terrace of the room during the Coffee Break)

Language of the presentation

English

Preferred session

Clinical Medicine

Összefoglaló szövege

Introduction: The use of artificial intelligence is rising in radiology by the day. One of the main prerequisites of a well-functioning AI is a great database of medical data, which was given during the COVID-19 pandemic.
Aims: We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration.
Methods: Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality.
Results: In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12-7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02-4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32-2.33, p < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 95% CI = 1.02-1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined.
Conclusion: AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification.
Funding: This research was funded by the Thematic Excellence Programme (2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Therapeutic Development and Bioimaging thematic programmes of the Semmelweis University, grant number 2020-4.1.1.-TKP2020.

University and Doctoral School

Semmelweis University, Doctoral School of Theoretical and Translational Medicine

Supervisor

Dr. Maurovich-Horvat Pál

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

5715

Start

10:15

End

10:30

Authors (legacy)

István Viktor Szabó 1, Judit Simon 1,2, Chiara Nardocci 1, Anna Sára Kardos 1,2, Norbert Nagy 1, Renad-Heyam Abdelrahman 1, Emese Zsarnóczay 1,2, Bence Fejér 1, Balázs Futácsi 1, Veronika Müller 3, Béla Merkely 2, Pál Maurovich-Horvat 1,2

1 Medical Imaging Centre, Semmelweis University
2 MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University
3 Department of Pulmonology, Semmelweis University