Automated malarial retinopathy detection using transfer learning and multi-camera retinal images

• Autorzy: Rajendra Kurup Aswathy , Wigdahl Jeff , Benson Jeremy , Martínez-Ramón Manel , Solíz Peter , Joshi Vinayak

Identyfikatory

DOI

10.1016/j.bbe.2022.12.003

• Języki publikacji: EN

Abstrakty

EN

Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed due to the presence of clinical symptoms also related to pneumonia, meningitis, or others. Therefore, patients go untreated for these pathologies, resulting in death or neurological disability. It is essential to have a low-cost and high-specificity diagnostic technique for CM detection, for which We developed a method based on transfer learning (TL). Models pre-trained with TL select the good quality retinal images, which are fed into another TL model to detect CM. This approach shows a 96% specificity with low-cost retinal cameras.

Słowa kluczowe

EN

cerebral malaria; malarial retinopathy; transfer learning; partial least squares; random forest; convolutional neural network

PL

malaria mózgowa; retinopatia; transfer nauki; las losowy; sieć neuronowa konwolucyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 1
• Strony: 109--123
• Opis fizyczny: Bibliogr. 78 poz., rys., tab., wykr.

Twórcy

autor

Rajendra Kurup Aswathy

• Department of Electrical Engineering, The University of New Mexico, NM, USA

autor

Wigdahl Jeff

• VisionQuest Biomedical Inc., Albuquerque, NM, USA

autor

Benson Jeremy

• Benson AI, Wilmington, DE, USA

autor

Martínez-Ramón Manel

• Department of Electrical Engineering, The University of New Mexico, NM, USA

autor

Solíz Peter

• VisionQuest Biomedical Inc., Albuquerque, NM, USA

autor

Joshi Vinayak

• VisionQuest Biomedical Inc., Albuquerque, NM, USA

Bibliografia

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• [1] Seydel KB, Kampondeni SD, Valim C, Potchen MJ, Milner DA, Muwalo FW, et al. Brain swelling and death in children with cerebral malaria. N Engl J Med 2015;372(12):1126-37.
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• [14] Cabrera PR, Talavera IR, Castillo ML, Valls RA, Morales JL. Detachment of retinal pigment epithelium in retinopathy due to malaria. Archivos de la Sociedad Española de Oftalmología (English Edition) 2018;93(8):406-10.
• [15] Small DS, Taylor TE, Postels DG, Beare NA, Cheng J, MacCormick IJ, et al. Evidence from a natural experiment that malaria parasitemia is pathogenic in retinopathy-negative cerebral malaria. eLife 2017;6:e23699.
• [16] Villaverde C, Namazzi R, Shabani E, Park GS, Datta D, Hanisch B, et al. Retinopathy-positive cerebral malaria is associated with greater inflammation, blood-brain barrier breakdown, and neuronal damage than retinopathy-negative cerebral malaria. J Pediatric Infecti Dis Soc 2020;9(5):580-6.
• [17] White VA, Lewallen S, Beare NA, Molyneux ME, Taylor TE. Retinal pathology of pediatric cerebral malaria in Malawi. PLoS One 2009;4(1):e4317.
• [18] Barrera V, MacCormick IJC, Czanner G, Hiscott PS, White VA, Craig AG, et al. Neurovascular sequestration in paediatric P. falciparum malaria is visible clinically in the retina. eLife 2018;7:e32208.
• [19] Tu Z, Gormley J, Sheth V, Seydel KB, Taylor T, Beare N, et al. Cerebral malaria: insight into pathology from optical coherence tomography. Sci Rep 2021;11(1):1-12.
• [20] Lewallen S, Beare NAV. Causes and Significance of Malarial Retinopathy. New York, New York, NY: Springer; 2014. p. 1-11.
• [21] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick IJ, et al. Automated detection of malarial retinopathy in digital fundus images for improved diagnosis in Malawian children with clinically defined cerebral malaria. Sci Rep 2017;7 (1):1-12.
• [22] MacCormick IJC, Zhang B, Hill D, Cordeiro MF, Small DS. A proposed theoretical framework for retinal biomarkers, Alzheimer’s & Dementia: Diagnosis. Assess Dis Monit 2022;14 (1):e12327.
• [23] Padhy SK, Sahu S, Govindahari V. Retinopathy secondary to uncomplicated plasmodium vivax malaria. Ophthalmic Surg, Lasers ImagRetina 2021;52(1):50-1.
• [24] MacCormick IJ, Beare NA, Taylor TE, Barrera V, White VA, Hiscott P, et al. Cerebral malaria in children: using the retina to study the brain. Brain 2014;137(8):2119-42.
• [25] Trivedi S, Chakravarty A. Neurological complications of malaria. Current Neurol Neurosci Rep 2022:1-15.
• [26] White VA, Lewallen S, Beare N, Kayira K, Carr RA, Taylor TE. Correlation of retinal haemorrhages with brain haemorrhages in children dying of cerebral malaria in Malawi. Trans R Soc Trop Med Hyg 2001;95(6):618-21.
• [27] MacCormick IJ, Barrera V, Beare NA, Czanner G, Potchen M, Kampondeni S, et al. How does blood-retinal barrier breakdown relate to death and disability in pediatric cerebral malaria? J Infect Dis 2022;225(6):1070-80.
• [28] White VA, Barrera V, MacCormick IJC. Ocular pathology of cerebral malaria. Malaria Immunology. Springer; 2022. p. 749-63.
• [29] Song X, Wei W, Cheng W, Zhu H, Wang W, Dong H, et al. Cerebral malaria induced by plasmodium falciparum: clinical features, pathogenesis, diagnosis, and treatment. Front Cell Infect Microbiol 2022:1033.
• [30] MacCormick IJC, Lewallen S, Beare N, Harding SP. Measuring the impact of malaria on the living human retina. Malaria Immunology. Springer; 2022. p. 731-48.
• [31] Nortey LN, Anning AS, Nakotey GK, Ussif AM, Opoku YK, Osei SA, et al. Genetics of cerebral malaria: pathogenesis, biomarkers and emerging therapeutic interventions. Cell Biosci 2022;12(1):1-19.
• [32] Singh J, Verma R, Tiwari A, Mishra D, Singh H. Retinopathy as a prognostic marker in cerebral malaria. Indian Pediatr 2016;53(4):315-7.
• [33] Swamy L, Beare NA, Okonkwo O, Mahmoud TH. Funduscopy in cerebral malaria diagnosis: An international survey of practice patterns. Am J Trop Med Hygiene 2018;98(2):516.
• [34] Kurup A, Soliz P, Nemeth S, Joshi V. Automated detection of malarial retinopathy using transfer learning. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). p. 18-21.
• [35] Beare NA, Harding SP, Taylor TE, Lewallen S, Molyneux ME. Perfusion abnormalities in children with cerebral malaria and malarial retinopathy. J Infect Dis 2009;199(2):263-71.
• [36] Zhao Y, MacCormick IJC, Parry DG, Leach S, Beare NAV, Harding SP, et al. Automated detection of leakage in fluorescein angiography images with application to malarial retinopathy. Sci Rep 2015;5(1):1-12.
• [37] Zhao Y, Zheng Y, Liu Y, Yang J, Zhao Y, Chen D, et al. Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy. IEEE Trans Med Imaging 2016;36(1):51-63.
• [38] Yan Q, Zhao Y, Zheng Y, Liu Y, Zhou K, Frangi A, et al. Automated retinal lesion detection via image saliency analysis. Med Phys 2019;46(10):4531-44.
• [39] Li W, Fang W, Wang J, He Y, Deng G, Ye H, et al. A weakly supervised deep learning approach for leakage detection in fluorescein angiography images. Transl Vision Sci Technol 2022;11(3). 9-9.
• [40] Joshi VS, Maude RJ, Reinhardt JM, Tang L, Garvin MK, Sayeed AA, et al. Automated detection of malarial retinopathy-associated retinal hemorrhages. Investigat Ophthalmol Visual Sci 2012;53(10):6582-8.
• [41] Joshi V, Agurto C, Barriga S, Nemeth S, Soliz P, MacCormick I, et al. Automated detection of retinal whitening in malarial retinopathy. In: Tourassi GD, S.G.A. III. editors, Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785, International Society for Optics and Photonics, SPIE; 2016, p. 633 -39.
• [42] Joshi V, Wigdahl J, Nemeth S, Manda C, Lewallen S, Taylor T, et al. Automated detection of malarial retinopathy in retinal fundus images obtained in clinical settings. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2018. p. 5950-3.
• [43] Saleem M, Akram MU. Detection of hemorrhages for diagnosis of malarial retinopathy. In: 2014 Cairo International Biomedical Engineering Conference (CIBEC). p. 141-4.
• [44] Ashraf A, Akram MU, Sheikh SA, Abbas S. Detection of macular whitening and retinal hemorrhages for diagnosis of malarial retinopathy. In: 2015 IEEE International Conference on Imaging Systems and Techniques (IST). p. 1-5.
• [45] Son J, Shin JY, Kim HD, Jung K-H, Park KH, Park SJ. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images. Ophthalmology 2020;127(1):85-94.
• [46] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision (IJCV) 2015;115(3):211-52.
• [47] Benson J, Carrillo H, Wigdahl J, Nemeth S, Maynard J, Zamora G. Transfer learning for diabetic retinopathy. Medical Imaging 2018: Image Processing, vol. 10574. International Society for Optics and Photonics; 2018. p. 105741Z.
• [48] Guo Y, Wang Y, Yang H, Zhang J, Sun Q. Dual-attention efficientnet based on multi-view feature fusion for cervical squamous intraepithelial lesions diagnosis. Biocybernet Biomed Eng 2022;42(2):529-42.
• [49] Alnussairi MHD, _ Ibrahim AA. Malaria parasite detection using deep learning algorithms based on (cnns) technique. Comput Electr Eng 2022;103:108316.
• [50] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y. editors. 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
• [51] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
• [52] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Informat Process Syst 2012;25.
• [53] Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 1-9.
• [54] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016. p. 2818-26.
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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)