Literature DB >> 1103955

Reading chest radiographs for pneumoconiosis by computer.

J R Jagoe, K A Paton.   

Abstract

Computer programs for measuring simple pneumoconiosis in radiographs are described and assessed. The 36 films studied had been read by 11 skilled human observers and a 'radiological score' of pneumoconiotic severity was therefore available for each film. The computer assigns to each square grid of side 3-6 mm a measure which reflects the unevenness of the density distribution in that grid. The 'computed score' is defined as the mean diversity over all relevant grids in both lung fields. On the set of 36 films the correlation between radiological score and computed score was 0-88. By contrast, the correlation between the score assigned by a single observer and the average of the scores assigned by the other 10 was in the range 0-95 to 0-98. The program can use the computed score to classify a film into one of the four major International Labour Office (ILO) U/C categories, the success rate of this process being 80% compared with those quoted by other workers in the range 45%-65%. If the films used in this study be typical, then the program described may form the basis of an automatic method for measuring pneumoconiosis in epidemiological work.

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Year:  1975        PMID: 1103955      PMCID: PMC1008075          DOI: 10.1136/oem.32.4.267

Source DB:  PubMed          Journal:  Br J Ind Med        ISSN: 0007-1072


  2 in total

1.  Computer classification of pneumoconiosis from radiographs of coal workers.

Authors:  E L Hall; W O Crawford; F E Roberts
Journal:  IEEE Trans Biomed Eng       Date:  1975-11       Impact factor: 4.538

2.  AN EXPERIMENT IN FILM READING.

Authors:  F D LIDDELL
Journal:  Br J Ind Med       Date:  1963-10
  2 in total
  5 in total

1.  An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs.

Authors:  Peichun Yu; Hao Xu; Ying Zhu; Chao Yang; Xiwen Sun; Jun Zhao
Journal:  J Digit Imaging       Date:  2011-06       Impact factor: 4.056

2.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; T Ishida; T Kobayashi
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

Review 3.  Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.

Authors:  Liton Devnath; Peter Summons; Suhuai Luo; Dadong Wang; Kamran Shaukat; Ibrahim A Hameed; Hanan Aljuaid
Journal:  Int J Environ Res Public Health       Date:  2022-05-25       Impact factor: 4.614

4.  Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods.

Authors:  Eiichiro Okumura; Ikuo Kawashita; Takayuki Ishida
Journal:  Radiol Phys Technol       Date:  2014-01-12

5.  Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays.

Authors:  Liton Devnath; Zongwen Fan; Suhuai Luo; Peter Summons; Dadong Wang
Journal:  Int J Environ Res Public Health       Date:  2022-09-06       Impact factor: 4.614

  5 in total

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