Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile

MacCormick, Ian J. C.; Williams, Bryan; Zheng, Yalin; Li, Kun; Al-Bander, Baidaa; Czanner, Silvester; Cheeseman, Rob; Willoughby, Colin E.; Brown, Emery N.; Spaeth, George L.; Czanner, Gabriela

doi:10.1371/journal.pone.0209409

Cited by 39 publications

(40 citation statements)

References 25 publications

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“…The importance of modeling the effects of groupings or covariates in shape or image data is becoming increasingly recognized, e.g., a bootstrapped response-based imputation modeling (BRIM) of facial shape [1], a linear mixed model of optic disk shape [2], or variational auto-encoders more generally (see, e.g., [3][4][5]). Multilevel principal components analysis (mPCA) has also been shown [6][7][8][9][10] to provide an efficient method of modeling shape and image texture in such cases.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Farnell

Galloway

Zhurov

et al. 2020

Communications in Computer and Information Science

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Here we study the effects of age on facial shape in adolescents by using a method called multilevel principal components analysis (mPCA). An associated multilevel multivariate probability distribution is derived and expressions for the (conditional) probability of age-group membership are presented. This formalism is explored via Monte Carlo (MC) simulated data in the first dataset; where age is taken to increase the overall scale of a three-dimensional facial shape represented by 21 landmark points and all other "subjective" variations are related to the width of the face. Eigenvalue plots make sense and modes of variation correctly identify these two main factors at appropriate levels of the mPCA model. Component scores for both single-level PCA and mPCA show a strong trend with age. Conditional probabilities are shown to predict membership by age group and the Pearson correlation coefficient between actual and predicted group membership is r = 0.99. The effects of outliers added to the MC training data are reduced by the use of robust covariance matrix estimation and robust averaging of matrices. These methods are applied to another dataset containing 12 GPA-scaled (3D) landmark points for 195 shapes from 27 white, male schoolchildren aged 11 to 16 years old. 21% of variation in the shapes for this dataset was accounted for by age. Mode 1 at level 1 (age) via mPCA appears to capture an elongation in facial shape with age, which is consistent with age-related shape changes in children. Component scores for both single-level PCA and mPCA again show a distinct trend with age. Conditional probabilities are again shown to reflect membership by age group and the Pearson correlation coefficient is given by r = 0.63 in this case. These analyses are an excellent first test of the ability of multilevel statistical methods to model age-related changes in facial shape in adolescents.

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Section: Introductionmentioning

confidence: 99%

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Farnell

Galloway

Zhurov

et al. 2020

Communications in Computer and Information Science

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“…In some studies, the research question may allow reducing the high dimensionality of imaging data so that the spatial statistical approach is beneficial to use. 6 recommendations Our recommendations are driven by two primary concerns. First, there is low utilisation of spatial information in reviewed studies.…”

Section: Discussionmentioning

confidence: 99%

Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

et al. 2020

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BackgroundClinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies within the retina. Longitudinally collected images can show how clinical events at one point can affect the retina over time. In this review, we aimed to assess statistical approaches to spatial and spatio-temporal data in retinal images. We also review the spatio-temporal modelling approaches used in other medical image types.MethodsWe conducted a comprehensive literature review of both spatial or spatio-temporal approaches and non-spatial approaches to the statistical analysis of retinal images. The key methodological and clinical characteristics of published papers were extracted. We also investigated whether clinical variables and spatial correlation were accounted for in the analysis.ResultsThirty-four papers that included retinal imaging data were identified for full-text information extraction. Only 11 (32.4%) papers used spatial or spatio-temporal statistical methods to analyse images, others (23 papers, 67.6%) used non-spatial methods. Twenty-eight (82.4%) papers reported images collected cross-sectionally, while 6 (17.6%) papers reported analyses on images collected longitudinally. In imaging areas outside of ophthalmology, 19 papers were identified with spatio-temporal analysis, and multiple statistical methods were recorded.ConclusionsIn future statistical analyses of retinal images, it will be beneficial to clearly define and report the spatial distributions studied, report the spatial correlations, combine imaging data with clinical variables into analysis if available, and clearly state the software or packages used.

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“…Traditional machine learning models have also been applied for glaucoma prediction [ 9 , 10 , 11 ]. Some papers suggested segmentation of areas with abnormalities on optical images [ 12 , 13 , 14 , 15 ]. However, interpretations of “individual prediction” for glaucoma diagnosis remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation

Park

Cho

et al. 2021

Diagnostics

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The aim is to develop a machine learning prediction model for the diagnosis of glaucoma and an explanation system for a specific prediction. Clinical data of the patients based on a visual field test, a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, a general examination including an intraocular pressure (IOP) measurement, and fundus photography were provided for the feature selection process. Five selected features (variables) were used to develop a machine learning prediction model. The support vector machine, C5.0, random forest, and XGboost algorithms were tested for the prediction model. The performance of the prediction models was tested with 10-fold cross-validation. Statistical charts, such as gauge, radar, and Shapley Additive Explanations (SHAP), were used to explain the prediction case. All four models achieved similarly high diagnostic performance, with accuracy values ranging from 0.903 to 0.947. The XGboost model is the best model with an accuracy of 0.947, sensitivity of 0.941, specificity of 0.950, and AUC of 0.945. Three statistical charts were established to explain the prediction based on the characteristics of the XGboost model. Higher diagnostic performance was achieved with the XGboost model. These three statistical charts can help us understand why the machine learning model produces a specific prediction result. This may be the first attempt to apply “explainable artificial intelligence” to eye disease diagnosis.

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Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile

Cited by 39 publications

References 25 publications

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation

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