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Managing Interstitial Lung Diseases with Computer-Aided Visualization

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Hybrid Artificial Intelligence and IoT in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 209))

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Abstract

Pulmonary interstitial pathology and subsequent diagnosis represent a significant component of pulmonary medical practice [97.9/100000 cases]. Complexity in differential diagnosis is added due to the individual pathological entities’ low prevalence, very similar imaging aspects and individual variations. Current approaches almost always require a multi-disciplinary approach, on a case-by-case basis, with a team of high expertise and experienced medical practitioners. Moreover, human diagnosis error rate is unsuitably high (~20–30%) (Trusculescu et al. in European Radiology 30:6285–6292, 2020), with severe consequences for the patient’s evolution and prognosis. High-resolution computer imaging together with biopsy form the diagnosis foundation, yet often the biopsy is absent, creating the need for accurate diagnosis based solely on visualization. Recent years have proven that a mixture of computer enhancements and medical expertise are a synergistic and precise approach. Computer techniques span two categories: learning and discovery, each working either supervised or unsupervised; however, their output is largely data-centric. A more novel tactic is using visual aids in which data is processed and abstracted in the background, providing helpful hints and concentrated information to allow autonomous human conclusions. The valid and flexible modeling of diffuse interstitial lung diseases (DILD) is especially important in idiopathic pulmonary fibrosis (IPF), in which the accepted computer tomography diagnosis criteria allow extremely diverse individual variations. Current advances strictly structure the criteria, yet the four categories: typical, probable, indeterminate and most consistent with non-UIP (usual interstitial pneumonia) span a vast pattern array. Since IPF has such a dire prognosis (76, 8% mortality rate at 3 years), the need for good pattern recognition and discovery is imperative to allow proper patient management.

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Abbreviations

AEP:

Acute eosinophilic pneumonia

AIP:

Acute interstitial pneumonia

BAL:

Bronchoalveolar lavage

CAD:

Computer-aided diagnosis

CEP:

Chronic eosinophilic pneumonia

COP:

Cryptogenic organizing pneumonia

CTD:

Connective tissue disease

DILD:

Diffuse interstitial lung disease

DIP:

Desquamative interstitial pneumonia

EBB:

Endobronchial biopsy

EBUS–TBNA:

Endobronchial ultrasound with transbronchial needle aspiration

HP:

Hypersensitivity pneumonia

HRCT:

High-resolution computer tomography

HSP:

Hypersensitivity pneumonia

GERD:

Gastroesophageal reflux disease

IT:

Information technology

IIP:

Interstitial idiopathic pneumonia

ILD:

Interstitial lung disease

IPF:

Idiopathic pulmonary fibrosis

LAM:

Lymphangioleiomyomatosis

LIP:

Lymphoid interstitial pneumonia

MDD:

Multi-disciplinary discussion

NSIP:

Idiopathic non-specific interstitial pneumonia

PAH:

Pulmonary arterial hypertension

PLCH:

Pulmonary langerhans cell histiocytosis

PPFE:

Pleuroparenchymal fibroelastosis

RBILD:

Respiratory bronchiolitis interstitial lung disease

SLB:

Surgical lung biopsy

SPL:

Secondary pulmonary lobule

TBLB:

Transbronchial lung biopsy

TBLC:

Transbronchial lung cryobiopsy

UIIP:

Unclassifiable idiopathic interstitial pneumonia

UIP:

Usual interstitial pneumonia

VEGF-D:

Vascular endothelial growth factor D

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Authors and Affiliations

  1. Department of Pneumology, Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), “Victor Babeș” University of Medicine and Pharmacy, Eftimie Murgu Sq. no. 2, 300041, Timișoara, RO, Romania

    Adriana Trușculescu, Emanuela Tudorache & Cristian Oancea

  2. Department of Computer and Information Technology—Automation and Computers Faculty, “Politehnica” University of Timisoara, Vasile Pârvan Blvd. No. 2, 300223, Timișoara, RO, Romania

    Laura Broască & Versavia Maria Ancușa

  3. Department of Radiology, Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), “Victor Babeș” University of Medicine and Pharmacy, Eftimie Murgu, Sq. no. 2, 300041, Timișoara, RO, Romania

    Diana Manolescu

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  1. Adriana Trușculescu
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Correspondence to Diana Manolescu .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University, Majitar, Sikkim, India

    Akash Kumar Bhoi

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  3. Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Mihir Narayana Mohanty

  4. Graduate Program on Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

    Victor Hugo C. de Albuquerque

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Trușculescu, A., Broască, L., Ancușa, V.M., Manolescu, D., Tudorache, E., Oancea, C. (2021). Managing Interstitial Lung Diseases with Computer-Aided Visualization. In: Kumar Bhoi, A., Mallick, P.K., Narayana Mohanty, M., Albuquerque, V.H.C.d. (eds) Hybrid Artificial Intelligence and IoT in Healthcare. Intelligent Systems Reference Library, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2972-3_12

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