Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Version 1 : Received: 2 August 2022 / Approved: 4 August 2022 / Online: 4 August 2022 (04:14:44 CEST)

How to cite: Trușculescu, A.A.; Manolescu, D.L.; Broască, L.; Ancușa, V.M.; Ciocârlie, H.; Pescaru, C.C.; Vaștag, E.; Oancea, C.I. Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks. Preprints 2022, 2022080099 (doi: 10.20944/preprints202208.0099.v1). Trușculescu, A.A.; Manolescu, D.L.; Broască, L.; Ancușa, V.M.; Ciocârlie, H.; Pescaru, C.C.; Vaștag, E.; Oancea, C.I. Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks. Preprints 2022, 2022080099 (doi: 10.20944/preprints202208.0099.v1).

Abstract

Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

Keywords

Interstitial lung disease; Diffuse interstitial lung disease; Idiopathic pulmonary fibrosis; High-resolution computed tomography; Complex Networks; Computer aided diagnosis

Subject

MEDICINE & PHARMACOLOGY, Other

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