In Ophthalmology science
PURPOSE : To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging.
DESIGN : An algorithm was used on data from a prospective natural history study of MacTel for classification development.
SUBJECTS : A total of 1733 participants enrolled in an international natural history study of MacTel.
METHODS : The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity.
MAIN OUTCOME MEASURES : The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes.
RESULTS : The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed.
CONCLUSIONS : This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients.
FINANCIAL DISCLOSURES : Proprietary or commercial disclosure may be found after the references.
Chew Emily Y, Peto Tunde, Clemons Traci E, Sallo Ferenc B, Pauleikhoff Daniel, Leung Irene, Jaffe Glenn J, Heeren Tjebo F C, Egan Catherine A, Charbel Issa Peter, Balaskas Konstantinos, Holz Frank G, Gaudric Alain, Bird Alan C, Friedlander Martin
2023-Jun
BCVA, best-corrected visual acuity, BLR, blue light reflectance, CART, Classification and Regression Trees, CF, color fundus, Classification, Classification and Regression Trees (CART), EZ, ellipsoid zone, FAF, fundus autoflorescence, FLIO, fluorescence lifetime imaging ophthalmoscopy, MacTel, macular telangiectasia type 2, Machine learning, Macular telangiectasia type 2, NHOR, natural history observation registry, NHOS, natural history observation study, Neurovascular degeneration, OCTA, OCT angiography, SD-OCT, spectral domain-OCT, VA, visual acuity
