A Cloud-based Solution for Rapid and Smart Neuropathy Detection

Cloud hosting of software allows easy and quick access to a wider community. In this paper, we propose a cloud-based solution for a smart neuropathy detection system that will benefit the community in three ways. Medical research in the field of neuropathy will advance rapidly due to ubiquitous access to the software that will provide quick image analysis results. Doctors will be able to receive instantaneous results of their patients' corneal scans without going through the manual tracing. Lastly, the software has the potential to become a standard for neuropathy research findings and will allow for research collaboration on neuropathy. The prediction system requires corneal nerve images captured from a corneal confocal microscope and is composed of three parts. The images first undergo nerve segmentation, followed by feature extraction and neuropathy prediction.

[1]  Adnan Khan,et al.  Corneal Confocal Microscopy: An Imaging Endpoint for Axonal Degeneration in Multiple Sclerosis. , 2017, Investigative ophthalmology & visual science.

[2]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[3]  Rongxing Lu,et al.  PPDP: An efficient and privacy-preserving disease prediction scheme in cloud-based e-Healthcare system , 2018, Future Gener. Comput. Syst..

[4]  Sandeep K. Sood,et al.  Cloud-centric IoT based disease diagnosis healthcare framework , 2017, J. Parallel Distributed Comput..

[5]  J. Dinesh Peter,et al.  IOT based sustainable diabetic retinopathy diagnosis system , 2020, Sustain. Comput. Informatics Syst..

[6]  Sébastien Ourselin,et al.  GIFT-Cloud: A data sharing and collaboration platform for medical imaging research , 2017, Comput. Methods Programs Biomed..

[7]  R. Varatharajan,et al.  Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier , 2018, Future Gener. Comput. Syst..

[8]  Musaed Alhussein,et al.  Cloud based framework for Parkinson's disease diagnosis and monitoring system for remote healthcare applications , 2017, Future Gener. Comput. Syst..

[9]  Gregory D. Hager,et al.  Artificial Intelligence for Social Good , 2019, ArXiv.

[10]  Uvais Qidwai,et al.  Classification of Corneal Nerve Images using Machine Learning Techniques , 2019, International Journal of Integrated Engineering.

[11]  Nan Yang,et al.  A disease diagnosis and treatment recommendation system based on big data mining and cloud computing , 2018, Inf. Sci..

[12]  Uvais Qidwai,et al.  Neuro-Fuzzy Classifier for Corneal Nerve Images , 2018, 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[13]  R. Malik,et al.  Treatment of painful diabetic neuropathy , 2015, Therapeutic advances in chronic disease.

[14]  Yanchun Zhang,et al.  Smart electronic gastroscope system using a cloud-edge collaborative framework , 2019, Future Gener. Comput. Syst..

[15]  O. Suchowersky,et al.  Evidence for small fiber neuropathy in early Parkinson's disease. , 2016, Parkinsonism & related disorders.

[16]  Natasha Correia Queiroz Lino,et al.  Clinical Decision Support Based on OWL Queries in a Knowledge-as-a-Service Architecture , 2018, RuleML+RR.

[17]  J. Mclaughlin,et al.  Corneal confocal microscopy for the diagnosis of diabetic autonomic neuropathy , 2015, Muscle & nerve.

[18]  Liming Wang,et al.  An artificial intelligence platform for the multihospital collaborative management of congenital cataracts , 2017, Nature Biomedical Engineering.

[19]  Chaithanya A Ramachandra,et al.  The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes , 2019, Diabetes technology & therapeutics.

[20]  Mohammad A. Dabbah,et al.  An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images , 2017, IEEE Transactions on Biomedical Engineering.