Literature DB >> 3519523

Computerized search of chest radiographs for nodules.

W A Lampeter, J C Wandtke.   

Abstract

A computer program that recognizes potential pulmonary nodules in PA chest radiographs has been developed. This program produces a display of candidate nodules that require interpretation by a radiologist. Some false positives are rejected by a program, the Nodule Expert. Detection performance with and without Nodule Expert has been evaluated. Using the untrained program (no Nodule Expert), and after inspecting 45 candidate nodules, a radiologist may be confident that a nodule was inspected, if one was located by the program. When pattern recognition techniques are incorporated, the number of false positives presented for inspection is reduced. The radiologist must inspect, at most, 10 candidate nodules to be confident of having inspected a nodule, if one was located by the program. Concomitant with this decrease in the candidate nodule false-positive rate is a decrease in sensitivity (film true-positive rate) from 92 to 86%. This program was trained on candidate nodules from 37 radiographs and also tested on these 37. Some of the features used by the pattern classifier to classify candidate nodules are comparable to those used by human observers.

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Year:  1986        PMID: 3519523     DOI: 10.1097/00004424-198605000-00003

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  7 in total

1.  Computer-assisted test interpretation: considerations in patient care.

Authors:  S D Hillson; D P Connelly
Journal:  J Med Syst       Date:  1992-10       Impact factor: 4.460

2.  Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

Authors:  S G Armato; M L Giger; H MacMahon
Journal:  J Digit Imaging       Date:  1999-02       Impact factor: 4.056

Review 3.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

Review 4.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

Review 5.  Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.

Authors:  Bo Liu; Wenhao Chi; Xinran Li; Peng Li; Wenhua Liang; Haiping Liu; Wei Wang; Jianxing He
Journal:  J Cancer Res Clin Oncol       Date:  2019-11-30       Impact factor: 4.553

6.  Automatic lung nodule detection using profile matching and back-propagation neural network techniques.

Authors:  S C Lo; M T Freedman; J S Lin; S K Mun
Journal:  J Digit Imaging       Date:  1993-02       Impact factor: 4.056

7.  Lung segmentation in digital radiographs.

Authors:  E Pietka
Journal:  J Digit Imaging       Date:  1994-05       Impact factor: 4.056

  7 in total

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