http://www.cnr.it/ontology/cnr/individuo/prodotto/ID24214

Computer-aided Recognition of Emphysema on Digital Chest Radiography (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Computer-aided Recognition of Emphysema on Digital Chest Radiography (Articolo in rivista) (literal)

Anno

2010-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1016/j.ejrad.2010.08.021 (literal)

Alternative label

Miniati, Massimo [ 1 ]; Coppini, Giuseppe [ 2 ]; Monti, Simonetta [ 2 ]; Bottai, Matteo [ 3,4 ]; Paterni, Marco [ 2 ]; Ferdeghini, Ezio Maria [ 2 ] (2010)
Computer-aided Recognition of Emphysema on Digital Chest Radiography
in European journal of radiology; Elsevier, Amsterdam (Paesi Bassi)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Miniati, Massimo [ 1 ]; Coppini, Giuseppe [ 2 ]; Monti, Simonetta [ 2 ]; Bottai, Matteo [ 3,4 ]; Paterni, Marco [ 2 ]; Ferdeghini, Ezio Maria [ 2 ] (literal)

Pagina inizio

169 (literal)

Pagina fine

175 (literal)

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ID_PUMA: cnr.ifc/2011-A0-113Avilable online September 15th, 2010 (literal)

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80 (literal)

Rivista

European journal of radiology (Rivista)

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In: European Journal of Radiology, vol. XX pp. XX - XX. Elsevier, 2010. (literal)

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6 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

2 (literal)

Note

Scopu (literal)
ISI Web of Science (WOS) (literal)

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[ 1 ] Univ Florence, Dept Med & Surg Crit Care, I-50134 Florence, Italy; [ 2 ] CNR-IFC, Inst Clin Physiol, I-56124 Pisa, Italy Pisa ; [ 3 ] Karolinska Inst, Inst Environm Med, Biostat Unit, S-17177 Stockholm, Sweden; [ 4 ] Univ S Carolina, Div Biostat, Arnold Sch Publ Hlth, Columbia, SC 29208 USA (literal)

Titolo

Computer-aided Recognition of Emphysema on Digital Chest Radiography (literal)

Abstract

Background Computed tomography (CT) is the benchmark for diagnosis emphysema, but is costly and imparts a substantial radiation burden to the patient. Objective To develop a computer-aided procedure that allows recognition of emphysema on digital chest radiography by using simple descriptors of the lung shape. The procedure was tested against CT. Methods Patients (N=225), who had undergone postero-anterior and lateral digital chest radiographs and CT for diagnostic purposes, were studied and divided in a derivation (N=118) and in a validation sample (N=107). CT images were scored for emphysema using the picture-grading method. Simple descriptors that measure the bending characteristics of the lung profile on chest radiography were automatically extracted from the derivation sample, and applied to train a neural network to assign a probability of emphysema between 0 and 1. The diagnostic performance of the procedure was described by the area under the receiver operating characteristic curve (AUC). Results AUC was 0.985 (95% confidence interval, 0.965 to 0.998) in the derivation sample, and 0.975 (95% confidence interval, 0.936 to 0.998) in the validation sample. At a probability cutpoint of 0.55, the procedure yielded 92% sensitivity and 96% specificity in the derivation sample; 90% sensitivity and 97% specificity in the validation sample. False negatives on chest radiography had trace or mild emphysema on CT. Conclusions The computer-aided procedure is simple and inexpensive, and permits quick recognition of emphysema on digital chest radiographs (literal)

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