Decision Support Systems and Applications in Ophthalmology: Literature and Commercial Review Focused on Mobile Apps

The growing importance that mobile devices have in daily life has also reached health care and medicine. This is making the paradigm of health care change and the concept of mHealth or mobile health more relevant, whose main essence is the apps. This new reality makes it possible for doctors who are not specialist to have easy access to all the information generated in different corners of the world, making them potential keepers of that knowledge. However, the new daily information exceeds the limits of the human intellect, making Decision Support Systems (DSS) necessary for helping doctors to diagnose diseases and also help them to decide the attitude that has to be taken towards these diagnoses. These could improve the health care in remote areas and developing countries. All of this is even more important in diseases that are more prevalent in primary care and that directly affect the people’s quality of life, this is the case in ophthalmological problems where in first patient care a specialist in ophthalmology is not involved. The goal of this paper is to analyse the state of the art of DSS in Ophthalmology. Many of them focused on diseases affecting the eye’s posterior pole. For achieving the main purpose of this research work, a literature review and commercial apps analysis will be done. The used databases and systems will be IEEE Xplore, Web of Science (WoS), Scopus, and PubMed. The search is limited to articles published from 2000 until now. Later, different Mobile Decision Support System (MDSS) in Ophthalmology will be analyzed in the virtual stores for Android and iOS. 37 articles were selected according their thematic (posterior pole, anterior pole, Electronic Health Records (EHRs), cloud, data mining, algorithms and structures for DSS, and other) from a total of 600 found in the above cited databases. Very few mobile apps were found in the different stores. It can be concluded that almost all existing mobile apps are focused on the eye’s posterior pole. Among them, the most intended are for diagnostic of diabetic retinopathy. The primary market niche of the commercial apps is the general physicians.

[1]  Li Zhang,et al.  An intelligent mobile based decision support system for retinal disease diagnosis , 2014, Decis. Support Syst..

[2]  Konstantina S. Nikita,et al.  A hybrid Decision Support System for the risk assessment of retinopathy development as a long term complication of Type 1 Diabetes Mellitus , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[3]  X.-P. Hu,et al.  Hot spot detection based on feature space representation of visual search in medical imaging , 2003, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003..

[4]  R. Vijayalakshmi,et al.  Visual dictionary: A decision support tool for DR pathology detection on POI , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[5]  H. Bourlard,et al.  S E a R C H , 2002 .

[6]  Craig B. Stanford,et al.  Scopus , 2009, Experimental Neurology.

[7]  A. Vainoras,et al.  Network based clinical decision support system , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[8]  Srinivasan Parthasarathy,et al.  Spatial Modeling and Classification of Corneal Shape , 2007, IEEE Transactions on Information Technology in Biomedicine.

[9]  P. L. Hildebrand,et al.  Adoption and perceptions of electronic health record systems by ophthalmologists: an American Academy of Ophthalmology survey. , 2008, Ophthalmology.

[10]  Juan Miguel García-Gómez,et al.  Mobile Clinical Decision Support Systems and Applications: A Literature and Commercial Review , 2013, Journal of Medical Systems.

[11]  Vidya Chitre,et al.  Three-level HAC on food borne disease and related treatment to help medical DSS , 2012, 2012 1st International Conference on Recent Advances in Information Technology (RAIT).

[12]  Yogesan Kanagasingam,et al.  Retinal image registration and comparison for clinical decision support. , 2012, The Australasian medical journal.

[13]  Prateek Prasanna,et al.  Decision support system for detection of diabetic retinopathy using smartphones , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[14]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic retinopathy: A review , 2013, Comput. Biol. Medicine.

[15]  T O Bola Odufuwa,et al.  Diagnostic decision support in ophthalmology , 2007 .

[16]  Kensaku Kawamoto,et al.  Service Oriented Architecture for Clinical Decision Support: A Systematic Review and Future Directions , 2014, Journal of Medical Systems.

[17]  L. Brooke The National Library of Medicine. , 1980, Hospital libraries.

[18]  Chia-Ling Tsai,et al.  Automated Retinal Image Analysis Over the Internet , 2008, IEEE Transactions on Information Technology in Biomedicine.

[19]  Tonya Hongsermeier,et al.  A pilot study of distributed knowledge management and clinical decision support in the cloud , 2013, Artif. Intell. Medicine.

[20]  Shivani Batra,et al.  Applying data mining techniques to standardized electronic health records for decision support , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[21]  Michelle Wilde IEEE Xplore Digital Library , 2016 .

[22]  Jos De Roo,et al.  Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support , 2013, Comput. Methods Programs Biomed..

[23]  U R Acharya,et al.  Decision support system for diabetic retinopathy using discrete wavelet transform. , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[24]  Azween Abdullah,et al.  E-healthcare and data management services in a cloud , 2011, 8th International Conference on High-capacity Optical Networks and Emerging Technologies.

[25]  Monique W. M. Jaspers,et al.  From an expert-driven paper guideline to a user-centred decision support system: A usability comparison study , 2013, Artif. Intell. Medicine.

[26]  InSook Cho,et al.  Design and implementation of a standards-based interoperable clinical decision support architecture in the context of the Korean EHR , 2010, Int. J. Medical Informatics.

[27]  P. Remagnino,et al.  Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier , 2013, 2013 International Conference on Computer Medical Applications (ICCMA).

[28]  Julian Quiroga,et al.  Support system for the preventive diagnosis of Hypertensive Retinopathy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[29]  Saifur Rahaman,et al.  Diabetes diagnosis decision support system based on symptoms, signs and risk factor using special computational algorithm by rule base , 2012, 2012 15th International Conference on Computer and Information Technology (ICCIT).

[30]  S. Bursell,et al.  Telemedicine and ocular health in diabetes mellitus , 2012, Clinical & experimental optometry.

[31]  J.S. Suri,et al.  Computer-Based Classification of Eye Diseases , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Kyoung-Yun Kim,et al.  Systematic causal knowledge acquisition using FCM Constructor for product design decision support , 2011, Expert Syst. Appl..

[33]  M. Madheswaran,et al.  An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images , 2012, Journal of Medical Systems.

[34]  Hermina J. M. Tabachneck-Schijf,et al.  Preventing knowledge transfer errors: Probabilistic decision support systems through the users' eyes , 2009, Int. J. Approx. Reason..

[35]  Lipika Samal,et al.  The good, the bad and the early adopters: providers' attitudes about a common, commercial EHR. , 2014, Journal of evaluation in clinical practice.

[36]  Kamesh Namuduri,et al.  A Decision Support Framework for Automated Screening of Diabetic Retinopathy , 2006, Int. J. Biomed. Imaging.

[37]  Gintautas Dzemyda,et al.  The use of information technologies for diagnosis in ophthalmology , 2006, Journal of telemedicine and telecare.

[38]  Jeroen S. de Bruin,et al.  Acceptability and difficulties of (fuzzy) decision support systems in clinical practice , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).