1
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Kang D, Wu H, Yuan L, Shi Y, Jin K, Grzybowski A. A Beginner's Guide to Artificial Intelligence for Ophthalmologists. Ophthalmol Ther 2024; 13:1841-1855. [PMID: 38734807 PMCID: PMC11178755 DOI: 10.1007/s40123-024-00958-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.
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Affiliation(s)
- Daohuan Kang
- Department of Ophthalmology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Hongkang Wu
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lu Yuan
- Department of Ophthalmology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yu Shi
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
- Zhejiang University School of Medicine, Hangzhou, China
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
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2
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Sonmez SC, Sevgi M, Antaki F, Huemer J, Keane PA. Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges. Br J Ophthalmol 2024:bjo-2024-325458. [PMID: 38925907 DOI: 10.1136/bjo-2024-325458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024]
Abstract
The rapid advancements in generative artificial intelligence are set to significantly influence the medical sector, particularly ophthalmology. Generative adversarial networks and diffusion models enable the creation of synthetic images, aiding the development of deep learning models tailored for specific imaging tasks. Additionally, the advent of multimodal foundational models, capable of generating images, text and videos, presents a broad spectrum of applications within ophthalmology. These range from enhancing diagnostic accuracy to improving patient education and training healthcare professionals. Despite the promising potential, this area of technology is still in its infancy, and there are several challenges to be addressed, including data bias, safety concerns and the practical implementation of these technologies in clinical settings.
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Affiliation(s)
| | - Mertcan Sevgi
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
| | - Fares Antaki
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
- The CHUM School of Artificial Intelligence in Healthcare, Montreal, Quebec, Canada
| | - Josef Huemer
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
- Department of Ophthalmology and Optometry, Kepler University Hospital, Linz, Austria
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, NIHR Moorfields Biomedical Research Centre, London, UK
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3
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Grzybowski A, Jin K, Wu H. Challenges of artificial intelligence in medicine and dermatology. Clin Dermatol 2024; 42:210-215. [PMID: 38184124 DOI: 10.1016/j.clindermatol.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Kai Jin
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongkang Wu
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
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4
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Jendle J, Adolfsson P, Choudhary P, Dovc K, Fleming A, Klonoff DC, Mader JK, Oliver N, Sherr JL, Šoupal J, Heinemann L. A narrative commentary about interoperability in medical devices and data used in diabetes therapy from an academic EU/UK/US perspective. Diabetologia 2024; 67:236-245. [PMID: 38041737 PMCID: PMC10789828 DOI: 10.1007/s00125-023-06049-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/05/2023] [Indexed: 12/03/2023]
Abstract
People living with diabetes have many medical devices available to assist with disease management. A critical aspect that must be considered is how systems for continuous glucose monitoring and insulin pumps communicate with each other and how the data generated by these devices can be downloaded, integrated, presented and used. Not only is interoperability associated with practical challenges, but also devices must adhere to all aspects of regulatory and legal frameworks. Key issues around interoperability in terms of data ownership, privacy and the limitations of interoperability include where the responsibility/liability for device and data interoperability lies and the need for standard data-sharing protocols to allow the seamless integration of data from different sources. There is a need for standardised protocols for the open and transparent handling of data and secure integration of data into electronic health records. Here, we discuss the current status of interoperability in medical devices and data used in diabetes therapy, as well as regulatory and legal issues surrounding both device and data interoperability, focusing on Europe (including the UK) and the USA. We also discuss a potential future landscape in which a clear and transparent framework for interoperability and data handling also fulfils the needs of people living with diabetes and healthcare professionals.
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Affiliation(s)
- Johan Jendle
- School of Medicine, Institute of Medical Sciences, Örebro University, Örebro, Sweden.
| | - Peter Adolfsson
- Department of Paediatrics, The Hospital of Halland Kungsbacka, Kungsbacka, Sweden
- Institute of Clinical Sciences, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Pratik Choudhary
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Klemen Dovc
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Department of Paediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's Hospital, Ljubljana, Slovenia
| | | | - David C Klonoff
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - Julia K Mader
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | | | - Jan Šoupal
- 3rd Department of Internal Medicine, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Lutz Heinemann
- Science Consulting in Diabetes GmbH, Düsseldorf, Germany
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5
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Gholami S, Lim JI, Leng T, Ong SSY, Thompson AC, Alam MN. Federated learning for diagnosis of age-related macular degeneration. Front Med (Lausanne) 2023; 10:1259017. [PMID: 37901412 PMCID: PMC10613107 DOI: 10.3389/fmed.2023.1259017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions. Experimental results indicate that FL strategies can achieve competitive performance similar to centralized models even though each local model has access to a portion of the training data. Notably, the Adaptive Personalization FL strategy stood out in our FL evaluations, consistently delivering high performance across all tests due to its additional local model. Furthermore, the study provides valuable insights into the efficacy of simpler architectures in image classification tasks, particularly in scenarios where data privacy and decentralization are critical using both encoders. It suggests future exploration into deeper models and other FL strategies for a more nuanced understanding of these models' performance. Data and code are available at https://github.com/QIAIUNCC/FL_UNCC_QIAI.
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Affiliation(s)
- Sina Gholami
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Jennifer I. Lim
- Department of Ophthalmology and Visual Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Theodore Leng
- Department of Ophthalmology, School of Medicine, Stanford University, Stanford, CA, United States
| | - Sally Shin Yee Ong
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Atalie Carina Thompson
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Minhaj Nur Alam
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
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6
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Zhao Z, Faghihroohi S, Yang J, Huang K, Navab N, Maier M, Nasseri MA. Unobtrusive biometric data de-identification of fundus images using latent space disentanglement. BIOMEDICAL OPTICS EXPRESS 2023; 14:5466-5483. [PMID: 37854552 PMCID: PMC10581784 DOI: 10.1364/boe.495438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/29/2023] [Accepted: 08/16/2023] [Indexed: 10/20/2023]
Abstract
With the incremental popularity of ophthalmic imaging techniques, anonymization of the clinical image datasets is becoming a critical issue, especially the fundus images, which would have unique patient-specific biometric content. Towards achieving a framework to anonymize ophthalmic images, we propose an image-specific de-identification method on the vascular structure of retinal fundus images while preserving important clinical features such as hard exudates. Our method calculates the contribution of latent code in latent space to the vascular structure by computing the gradient map of the generated image with respect to latent space and then by computing the overlap between the vascular mask and the gradient map. The proposed method is designed to specifically target and effectively manipulate the latent code with the highest contribution score in vascular structures. Extensive experimental results show that our proposed method is competitive with other state-of-the-art approaches in terms of identity similarity and lesion similarity, respectively. Additionally, our approach allows for a better balance between identity similarity and lesion similarity, thus ensuring optimal performance in a trade-off manner.
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Affiliation(s)
- Zhihao Zhao
- TUM School of Computation, Information and Technology , Technical University of Munich, Arcisstrasse 21, Munich, 80333, Germany
- Klinik und Poliklinik für Augenheilkunde, Technische Universität München, IsmaningerStr. 22, München, 81675, Germany
| | - Shahrooz Faghihroohi
- TUM School of Computation, Information and Technology , Technical University of Munich, Arcisstrasse 21, Munich, 80333, Germany
| | - Junjie Yang
- TUM School of Computation, Information and Technology , Technical University of Munich, Arcisstrasse 21, Munich, 80333, Germany
- Klinik und Poliklinik für Augenheilkunde, Technische Universität München, IsmaningerStr. 22, München, 81675, Germany
| | - Kai Huang
- School of Computer Science and Engineering, Sun Yat-Sen University, Panyu District 132, Guangzhou, 510006, China
| | - Nassir Navab
- TUM School of Computation, Information and Technology , Technical University of Munich, Arcisstrasse 21, Munich, 80333, Germany
| | - Mathias Maier
- Klinik und Poliklinik für Augenheilkunde, Technische Universität München, IsmaningerStr. 22, München, 81675, Germany
| | - M. Ali Nasseri
- Klinik und Poliklinik für Augenheilkunde, Technische Universität München, IsmaningerStr. 22, München, 81675, Germany
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7
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Oganov AC, Seddon I, Jabbehdari S, Uner OE, Fonoudi H, Yazdanpanah G, Outani O, Arevalo JF. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Surv Ophthalmol 2023; 68:905-919. [PMID: 37116544 DOI: 10.1016/j.survophthal.2023.04.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 04/30/2023]
Abstract
Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies.
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Affiliation(s)
- Anthony C Oganov
- Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA
| | - Ian Seddon
- College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Sayena Jabbehdari
- Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
| | - Ogul E Uner
- Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA
| | - Hossein Fonoudi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran
| | - Ghasem Yazdanpanah
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA
| | - Oumaima Outani
- Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
| | - J Fernando Arevalo
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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8
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Sun H, Plawinski J, Subramaniam S, Jamaludin A, Kadir T, Readie A, Ligozio G, Ohlssen D, Baillie M, Coroller T. A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs). PLoS One 2023; 18:e0280316. [PMID: 37410795 PMCID: PMC10325103 DOI: 10.1371/journal.pone.0280316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 12/27/2022] [Indexed: 07/08/2023] Open
Abstract
Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.
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Affiliation(s)
- Hanxi Sun
- Department of Statistics, Purdue University, West Lafayette, IN, United States of America
| | - Jason Plawinski
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Sajanth Subramaniam
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | | | | | - Aimee Readie
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Gregory Ligozio
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - David Ohlssen
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Mark Baillie
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
| | - Thibaud Coroller
- Novartis Pharmaceutical Corporation, East Hanover, New Jersey, United States of America
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9
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Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med 2023; 158:106848. [PMID: 37044052 DOI: 10.1016/j.compbiomed.2023.106848] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/21/2023] [Accepted: 03/30/2023] [Indexed: 04/14/2023]
Abstract
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
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Affiliation(s)
- Nazish Khalid
- Information Technology University, Lahore, Pakistan.
| | - Adnan Qayyum
- Information Technology University, Lahore, Pakistan.
| | - Muhammad Bilal
- Big Data Enterprise and Artificial Intelligence Lab (Big-DEAL), University of the West England, Bristol, United Kingdom.
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10
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Nitiéma P. Artificial Intelligence in Medicine: Text Mining of Health Care Workers' Opinions. J Med Internet Res 2023; 25:e41138. [PMID: 36584303 PMCID: PMC9919460 DOI: 10.2196/41138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 11/11/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research. OBJECTIVE We aimed to examine health care workers' opinions about the adoption and implementation of AI-powered technology in the health care industry. METHODS Data were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups. RESULTS Overall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine. CONCLUSIONS The adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies.
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Affiliation(s)
- Pascal Nitiéma
- Department of Information Systems, Arizona State University, Tempe, AZ, United States
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11
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Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2022; 2:100078. [PMID: 37846285 PMCID: PMC10577833 DOI: 10.1016/j.aopr.2022.100078] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 10/18/2023]
Abstract
Background The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
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Affiliation(s)
- Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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12
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Jeon M, Park H, Kim HJ, Morley M, Cho H. k-SALSA: k-anonymous synthetic averaging of retinal images via local style alignment. COMPUTER VISION - ECCV ... : ... EUROPEAN CONFERENCE ON COMPUTER VISION : PROCEEDINGS. EUROPEAN CONFERENCE ON COMPUTER VISION 2022; 13681:661-678. [PMID: 37525827 PMCID: PMC10388376 DOI: 10.1007/978-3-031-19803-8_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
The application of modern machine learning to retinal image analyses offers valuable insights into a broad range of human health conditions beyond ophthalmic diseases. Additionally, data sharing is key to fully realizing the potential of machine learning models by providing a rich and diverse collection of training data. However, the personallyidentifying nature of retinal images, encompassing the unique vascular structure of each individual, often prevents this data from being shared openly. While prior works have explored image de-identification strategies based on synthetic averaging of images in other domains (e.g. facial images), existing techniques face difficulty in preserving both privacy and clinical utility in retinal images, as we demonstrate in our work. We therefore introduce k-SALSA, a generative adversarial network (GAN)-based framework for synthesizing retinal fundus images that summarize a given private dataset while satisfying the privacy notion of k-anonymity. k-SALSA brings together state-of-the-art techniques for training and inverting GANs to achieve practical performance on retinal images. Furthermore, k-SALSA leverages a new technique, called local style alignment, to generate a synthetic average that maximizes the retention of fine-grain visual patterns in the source images, thus improving the clinical utility of the generated images. On two benchmark datasets of diabetic retinopathy (EyePACS and APTOS), we demonstrate our improvement upon existing methods with respect to image fidelity, classification performance, and mitigation of membership inference attacks. Our work represents a step toward broader sharing of retinal images for scientific collaboration. Code is available at https://github.com/hcholab/k-salsa.
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Affiliation(s)
- Minkyu Jeon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Korea University, Seoul, Republic of Korea
| | | | | | - Michael Morley
- Harvard Medical School, Boston, MA, USA
- Ophthalmic Consultants of Boston, Boston, MA, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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13
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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14
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Borgstadt JT, Kalpas EA, Pond HM. A Qualitative Thematic Analysis of Addressing the Why: An Artificial Intelligence (AI) in Healthcare Symposium. Cureus 2022; 14:e23704. [PMID: 35510027 PMCID: PMC9060766 DOI: 10.7759/cureus.23704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2022] [Indexed: 12/04/2022] Open
Abstract
Healthcare managers and clinicians are inefficient in the processes of workflows and documentation. The inefficiency is due in part by increasing demands of insurance companies, regulatory demands from the government, and human error. Artificial intelligence (AI) can improve healthcare processes by decreasing variability, thus improving patient and physician experience and patient outcomes. This project brings together a panel of five experts to discuss problems in medicine and some of the tools available through AI and technology to address these problems. The symposium modeled a "flipped classroom" format. The first five 20-minute modules were uploaded to a web-based platform for viewing in advance of the 60-minute moderated roundtable (Zoom, Zoom Video Communications, San Jose, CA, USA). The following themes emerged after reviewing the transcribed data: data privacy and access (N=3, number of times identified); process improvement (N=2); physician experience (N=1); value in data (N=2); and bias in healthcare and AI (N=3). For AI to become implemented on a large scale in healthcare, many areas will need continued discussion and research, including a continued look into how AI can add value to workflow and knowledge augmentation. In addition, standards for the implementation of AI and a methodical approach to the analysis of the effectiveness of algorithms coupled with training of healthcare professionals in the language of AI algorithms will be helpful to ensure that AI is integrated safely.
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15
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Sampson DM, Dubis AM, Chen FK, Zawadzki RJ, Sampson DD. Towards standardizing retinal optical coherence tomography angiography: a review. LIGHT, SCIENCE & APPLICATIONS 2022; 11:63. [PMID: 35304441 PMCID: PMC8933532 DOI: 10.1038/s41377-022-00740-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 05/11/2023]
Abstract
The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.
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Affiliation(s)
- Danuta M Sampson
- Surrey Biophotonics, Centre for Vision, Speech and Signal Processing and School of Biosciences and Medicine, The University of Surrey, Guildford, GU2 7XH, UK.
| | - Adam M Dubis
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Trust and UCL Institute of Ophthalmology, London, EC1V 2PD, UK
| | - Fred K Chen
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, Nedlands, Western Australia, 6009, Australia
- Department of Ophthalmology, Royal Perth Hospital, Perth, Western Australia, 6000, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, 3002, Australia
| | - Robert J Zawadzki
- Department of Ophthalmology & Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - David D Sampson
- Surrey Biophotonics, Advanced Technology Institute, School of Physics and School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH, UK
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16
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Gupta C, Johri I, Srinivasan K, Hu YC, Qaisar SM, Huang KY. A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks. SENSORS 2022; 22:s22052017. [PMID: 35271163 PMCID: PMC8915055 DOI: 10.3390/s22052017] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 02/28/2022] [Accepted: 02/28/2022] [Indexed: 02/04/2023]
Abstract
Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.
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Affiliation(s)
- Chaitanya Gupta
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India; (C.G.); (K.S.)
| | - Ishita Johri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India;
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India; (C.G.); (K.S.)
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan;
| | - Saeed Mian Qaisar
- Electrical and Computer Engineering Department, Effat University, Jeddah 22332, Saudi Arabia;
| | - Kuo-Yi Huang
- Department of Bio-Industrial Mechatronic Engineering, National Chung Hsing University, Taichung 402, Taiwan
- Correspondence:
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17
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Kumar H, Goh KL, Guymer RH, Wu Z. A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration. Clin Exp Optom 2022; 105:674-679. [PMID: 35073498 DOI: 10.1080/08164622.2021.2022961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
In recent years, there has been intense development of artificial intelligence (AI) techniques, which have the potential to improve the clinical management of age-related macular degeneration (AMD) and facilitate the prevention of irreversible vision loss from this condition. Such AI techniques could be used as clinical decision support tools to: (i) improve the detection of AMD by community eye health practitioners, (ii) enhance risk stratification to enable personalised monitoring strategies for those with the early stages of AMD, and (iii) enable early detection of signs indicative of possible choroidal neovascularisation allowing triaging of patients requiring urgent review. This review discusses the latest developments in AI techniques that show promise for these tasks, as well as how they may help in the management of patients being treated for choroidal neovascularisation and in accelerating the discovery of new treatments in AMD.
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Affiliation(s)
- Himeesh Kumar
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Kai Lyn Goh
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Zhichao Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
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18
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Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res 2021; 90:101034. [PMID: 34902546 DOI: 10.1016/j.preteyeres.2021.101034] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/14/2023]
Abstract
An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.
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19
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Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
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20
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Hussey P, Das S. A micro credential for interoperability. OPEN RESEARCH EUROPE 2021; 1:109. [PMID: 37645197 PMCID: PMC10446011 DOI: 10.12688/openreseurope.14083.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 08/31/2023]
Abstract
In the midst of a global pandemic the need for health and social care providers to commit to, and deliver on, integrated patient-centered care services has been accelerated. Globally, health and social care programme administrators are turning to digital devices and applications to provide supporting infrastructure which can offer safe access to health information at the point of care. Digitalisation is increasingly considered a key requirement to support diagnostics and therapeutic care services in health care delivery. The open source community are responding to this need to advance integrated care and digital services by providing targeted resources to address the interoperability challenge. Addressing interoperability in health systems is a core part of achieving sustainable enterprise wide integrated care. Using Open Innovation 2.0 methods for advancing knowledge on interoperability, this paper describes the development of a micro credential for knowledge transfer on interoperability created by the Centre for eIntegrated Care (CeIC). Designed and developed to signpost interested stakeholders to targeted material and build understanding and capacity on the topic. The design approach and initial resource content are explained through the lens of a specific research project funded by an Elite S Fellowship to advance leadership and standardisation for Information and Communications Technology (ICT) in Europe.
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Affiliation(s)
- Pamela Hussey
- CeIC, ADAPT, School of Computing, Dublin City University, Dublin, Dublin, D09W6Y4, Ireland
| | - Subhashis Das
- CeIC, ADAPT, School of Computing, Dublin City University, Dublin, Dublin, D09W6Y4, Ireland
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21
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Petzold A, Albrecht P, Balcer L, Bekkers E, Brandt AU, Calabresi PA, Deborah OG, Graves JS, Green A, Keane PA, Nij Bijvank JA, Sander JW, Paul F, Saidha S, Villoslada P, Wagner SK, Yeh EA. Artificial intelligence extension of the OSCAR-IB criteria. Ann Clin Transl Neurol 2021; 8:1528-1542. [PMID: 34008926 PMCID: PMC8283174 DOI: 10.1002/acn3.51320] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/31/2020] [Accepted: 01/03/2021] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.
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Affiliation(s)
- Axel Petzold
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
- Neuro‐ophthalmology Expert CenterAmsterdam UMCThe Netherlands
| | - Philipp Albrecht
- Department of NeurologyMedical FacultyHeinrich‐Heine UniversityDüsseldorfGermany
| | - Laura Balcer
- Departments of NeurologyPopulation Health and OphthalmologyNYU Grossman School of MedicineNew YorkUSA
| | | | | | - Peter A. Calabresi
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | | | | | - Ari Green
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Pearse A Keane
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
| | | | - Josemir W. Sander
- NIHR UCL Hospitals Biomedical Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Stichting Epilepsie Instellingen Nederland (SEIN)HeemstedeThe Netherlands
| | - Friedemann Paul
- Experimental and Clinical Research CenterMax Delbrück Center for Molecular Medicine and Charité – Universitätsmedizin Berlincorporate member of Freie Universität BerlinHumboldt‐Universität zu Berlin, and Berlin Institute of HealthGermany
| | - Shiv Saidha
- Department of NeurologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Pablo Villoslada
- Institut d’Investigacion Biomediques August Pi Sunyer (DIBAPS) and Hospital ClinicUniversity of BarcelonaBarcelonaSpain
| | - Siegfried K Wagner
- Moorfields Eye HospitalCity Road, The National Hospital for Neurology and NeurosurgeryQueen SquareUCL Queen Square Institute of NeurologyLondonUK
| | - E. Ann Yeh
- Division of NeurologyDepartment of PediatricsHospital for Sick ChildrenDivision of Neurosciences and Mental Health SickKids Research InstituteUniversity of TorontoCanada
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22
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Lee CS, Brandt JD, Lee AY. Big Data and Artificial Intelligence in Ophthalmology: Where Are We Now? OPHTHALMOLOGY SCIENCE 2021; 1:100036. [PMID: 36249294 PMCID: PMC9560652 DOI: 10.1016/j.xops.2021.100036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Cecilia S. Lee
- Correspondence: Cecilia S. Lee, MD, MS, University of Washington, Box 359607, 325 Ninth Avenue, Seattle, WA 98104.
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23
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Tseng RMWW, Gunasekeran DV, Tan SSH, Rim TH, Lum E, Tan GSW, Wong TY, Tham YC. Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives. Asia Pac J Ophthalmol (Phila) 2021; 10:299-306. [PMID: 34383721 DOI: 10.1097/apo.0000000000000400] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
ABSTRACT Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in ophthalmology. Despite the burgeoning reports on the development of new AI algorithms for detection and management of various eye diseases, few have reached the stage of regulatory approval for real-world implementation. To better enable real-world translation of AI systems, it is important to understand the demands, needs, and concerns of both health care professionals and patients, as providers and recipients of clinical care are impacted by these solutions. This review outlines the advantages and concerns of incorporating AI in ophthalmology care delivery, from both the providers' and patients' perspectives, and the key enablers for seamless transition to real-world implementation.
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Affiliation(s)
| | - Dinesh Visva Gunasekeran
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
| | | | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Duke-NUS Medical School, Singapore
| | | | - Gavin S W Tan
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Duke-NUS Medical School, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Duke-NUS Medical School, Singapore
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24
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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25
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INOMATA TAKENORI, SUNG JAEMYOUNG, NAKAMURA MASAHIRO, IWAGAMI MASAO, OKUMURA YUICHI, FUJIO KENTA, AKASAKI YASUTSUGU, FUJIMOTO KEIICHI, YANAGAWA AI, MIDORIKAWA-INOMATA AKIE, NAGINO KEN, EGUCHI ATSUKO, SHOKIROVA HURRRAMHON, ZHU JUN, MIURA MARIA, KUWAHARA MIZU, HIROSAWA KUNIHIKO, HUANG TIANXING, MOROOKA YUKI, MURAKAMI AKIRA. Cross-hierarchical Integrative Research Network for Heterogenetic Eye Disease Toward P4 Medicine: A Narrative Review. JUNTENDO MEDICAL JOURNAL 2021. [DOI: 10.14789/jmj.jmj21-0023-r] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- TAKENORI INOMATA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - JAEMYOUNG SUNG
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MASAHIRO NAKAMURA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | - MASAO IWAGAMI
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba
| | - YUICHI OKUMURA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KENTA FUJIO
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - YASUTSUGU AKASAKI
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KEIICHI FUJIMOTO
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - AI YANAGAWA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | | | - KEN NAGINO
- Department of Hospital Administration, Juntendo University Graduate School of Medicine
| | - ATSUKO EGUCHI
- Department of Hospital Administration, Juntendo University Graduate School of Medicine
| | | | - JUN ZHU
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MARIA MIURA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - MIZU KUWAHARA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - KUNIHIKO HIROSAWA
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - TIANXING HUANG
- Department of Ophthalmology, Juntendo University Graduate School of Medicine
| | - YUKI MOROOKA
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
| | - AKIRA MURAKAMI
- Department of Digital Medicine, Juntendo University Graduate School of Medicine
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