Machine Learning Techniques for Ophthalmic Data Processing: A Review

Sarhan, Mhd Hasan; Nasseri, M. Ali; Zapp, Daniel; Maier, Mathias; Lohmann, Chris P.; Navab, Nassir; Eslami, Abouzar

doi:10.1109/jbhi.2020.3012134

Cited by 51 publications

(24 citation statements)

References 95 publications

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“…Many developers of AI algorithms for detecting DR use the images from datasets like Kaggle, Messidor, or Eyepacs, which are not representative for human diversity ( 24 ). There is the chance of overfitting, which occurs when the underlying datasets are too homogenous and, therefore, prone to generalization problems ( 25 ). Therefore, AI systems may not perform well on diverse populations and increase the risk of misdiagnosis.…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

et al. 2021

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Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

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Section: Discussionmentioning

confidence: 99%

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

et al. 2021

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“…In Supplementary Table S1 , we have complied recent review articles detailing emerging examples of how statistical and ML methods are being utilized for clinical outcome prediction in major medical specialities. Applications are found in the fields of Anesthesiology [ 32 , 33 , 34 ], Dermatology [ 35 , 36 , 37 ], Emergency Medicine [ 38 , 39 ], Family Medicine [ 40 , 40 ], Internal Medicine [ 41 , 42 , 43 ], Interventional Radiology [ 44 , 45 ], Medical Genetics [ 46 ], Neurological Surgery [ 47 ], Neurology [ 48 , 49 , 50 ], Obstetrics and Gynecology [ 51 , 52 ], Ophthalmology [ 53 , 54 , 55 ], Orthopaedic Surgery [ 56 ], Otorhinolaryngology [ 57 , 58 ], Pathology [ 59 , 60 , 61 ], Pediatrics [ 62 ], Physical Medicine and Rehabilitation [ 63 , 64 ], Plastic and Reconstructive Surgery [ 65 , 66 ], Psychiatry [ 67 , 68 ], Radiation Oncology [ 69 , 70 ], Radiology [ 71 , 72 ], General Surgery [ 73 , 74 ], Cardiothoracic Surgery [ 75 , 76 ], Urology [ 77 , 78 ], Vascular Surgery [ 79 , 80 ]. These papers introduce terms describing ML models as ‘supervised’ or ‘unsupervised’.…”

Section: Emerging Methods and Emerging Applicationsmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit

Fullem

Cheng

et al. 2021

Emerging Topics in Life Sciences

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AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

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“…It is widely used in computer vision, bioinformatics, health care, business analysis, trend forecasting, and other fields. ML allows computers to learn from large samples of data and predict patterns that exist in the data (52,53). ML algorithms to help realize the future of improved health care by unleashing the potential of large biomedical and patient data sets are used in different research areas to predict and classify accurate results from test data (54).…”

Section: Research Frontiers and Trends In Rehabilitation Robotsmentioning

confidence: 99%

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

Xue

Yang

Deng

et al. 2022

Front. Public Health

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Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

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Machine Learning Techniques for Ophthalmic Data Processing: A Review

Cited by 51 publications

References 95 publications

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020

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