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Kamran SA, Hossain KF, Ong J, Waisberg E, Zaman N, Baker SA, Lee AG, Tavakkoli A. FA4SANS-GAN: A Novel Machine Learning Generative Adversarial Network to Further Understand Ophthalmic Changes in Spaceflight Associated Neuro-Ocular Syndrome (SANS). OPHTHALMOLOGY SCIENCE 2024; 4:100493. [PMID: 38682031 PMCID: PMC11046204 DOI: 10.1016/j.xops.2024.100493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/11/2024] [Accepted: 02/05/2024] [Indexed: 05/01/2024]
Abstract
Purpose To provide an automated system for synthesizing fluorescein angiography (FA) images from color fundus photographs for averting risks associated with fluorescein dye and extend its future application to spaceflight associated neuro-ocular syndrome (SANS) detection in spaceflight where resources are limited. Design Development and validation of a novel conditional generative adversarial network (GAN) trained on limited amount of FA and color fundus images with diabetic retinopathy and control cases. Participants Color fundus and FA paired images for unique patients were collected from a publicly available study. Methods FA4SANS-GAN was trained to generate FA images from color fundus photographs using 2 multiscale generators coupled with 2 patch-GAN discriminators. Eight hundred fifty color fundus and FA images were utilized for training by augmenting images from 17 unique patients. The model was evaluated on 56 fluorescein images collected from 14 unique patients. In addition, it was compared with 3 other GAN architectures trained on the same data set. Furthermore, we test the robustness of the models against acquisition noise and retaining structural information when introduced to artificially created biological markers. Main Outcome Measures For GAN synthesis, metric Fréchet Inception Distance (FID) and Kernel Inception Distance (KID). Also, two 1-sided tests (TOST) based on Welch's t test for measuring statistical significance. Results On test FA images, mean FID for FA4SANS-GAN was 39.8 (standard deviation, 9.9), which is better than GANgio model's mean of 43.2 (standard deviation, 13.7), Pix2PixHD's mean of 57.3 (standard deviation, 11.5) and Pix2Pix's mean of 67.5 (standard deviation, 11.7). Similarly for KID, FA4SANS-GAN achieved mean of 0.00278 (standard deviation, 0.00167) which is better than other 3 model's mean KID of 0.00303 (standard deviation, 0.00216), 0.00609 (standard deviation, 0.00238), 0.00784 (standard deviation, 0.00218). For TOST measurement, FA4SANS-GAN was proven to be statistically significant versus GANgio (P = 0.006); versus Pix2PixHD (P < 0.00001); and versus Pix2Pix (P < 0.00001). Conclusions Our study has shown FA4SANS-GAN to be statistically significant for 2 GAN synthesis metrics. Moreover, it is robust against acquisition noise, and can retain clear biological markers compared with the other 3 GAN architectures. This deployment of this model can be crucial in the International Space Station for detecting SANS. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada
| | - Khondker Fariha Hossain
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan
| | - Ethan Waisberg
- Department of Ophthalmology, University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada
| | - Salah A. Baker
- Department of Physiology and Cell Biology, University of Nevada School of Medicine, Reno, Nevada
| | - Andrew G. Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, Texas
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas
- Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas
- Department of Ophthalmology, University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Ophthalmology, Texas A&M College of Medicine, Texas
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada
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Waisberg E, Ong J, Lee AG. Prioritizing open science in space medicine: perspectives following the NASA "Transform to Open Science (TOPS)" Curriculum. Ir J Med Sci 2024; 193:1683-1685. [PMID: 38244174 DOI: 10.1007/s11845-024-03612-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/22/2024]
Abstract
The National Aeronautics and Space Administration (NASA) has recently made a long-term commitment towards fostering open science. The NASA Transform to Open Science (TOPS) initiative provides recommendations, best practices, and tools related to open science. The principles of open science include the transparent sharing of data, findings, and methods and is designed to accelerate the pace of discovery and foster collaboration. The goal of open science is to allow data, publications, software, and physical samples to be accessible to all, regardless of being a professional or an amateur. In this paper, we summarize several key points open science that were presented as part of NASA's Open Science 101 Module 1 at an in-person training event in Washington, D.C., and include how open science can be beneficial for researchers and society as a whole.
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Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, UK.
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, USA
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Bryan, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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Waisberg E, Ong J, Kamran SA, Masalkhi M, Paladugu P, Zaman N, Lee AG, Tavakkoli A. Generative artificial intelligence in ophthalmology. Surv Ophthalmol 2024:S0039-6257(24)00044-4. [PMID: 38762072 DOI: 10.1016/j.survophthal.2024.04.009] [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: 12/23/2022] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024]
Abstract
Generative AI has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep learning framework that has become a powerful technique in medicine, particularly in ophthalmology and image analysis. In this paper we review the current ophthalmic literature involving GANs, and highlight key contributions in the field. We briefly touch on ChatGPT, another application of generative AI, and its potential in ophthalmology. We also explore the potential uses for GANs in ocular imaging, with a specific emphasis on 3 primary domains: image enhancement, disease identification, and generating of synthetic data. PubMed, Ovid MEDLINE, Google Scholar were searched from inception to October 30, 2022 to identify applications of GAN in ophthalmology. A total of 40 papers were included in this review. We cover various applications of GANs in ophthalmic-related imaging including optical coherence tomography, orbital magnetic resonance imaging, fundus photography, and ultrasound; however, we also highlight several challenges, that resulted in the generation of inaccurate and atypical results during certain iterations. Finally, we examine future directions and considerations for generative AI in ophthalmology.
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Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom.
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, United States
| | - Sharif Amit Kamran
- School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Mouayad Masalkhi
- School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Phani Paladugu
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, United States; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States
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Ong J, Waisberg E, Masalkhi M, Suh A, Kamran SA, Paladugu P, Sarker P, Zaman N, Tavakkoli A, Lee AG. "Spaceflight-to-Eye Clinic": Terrestrial advances in ophthalmic healthcare delivery from space-based innovations. LIFE SCIENCES IN SPACE RESEARCH 2024; 41:100-109. [PMID: 38670636 DOI: 10.1016/j.lssr.2024.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/08/2024] [Indexed: 04/28/2024]
Abstract
The phrase "Bench-to-Bedside" is a well-known phrase in medicine, highlighting scientific discoveries that directly translate to impacting patient care. Key examples of translational research include identification of key molecular targets in diseases and development of diagnostic laboratory tests for earlier disease detection. Bridging these scientific advances to the bedside/clinic has played a meaningful impact in numerous patient lives. The spaceflight environment poses a unique opportunity to also make this impact; the nature of harsh extraterrestrial conditions and medically austere and remote environments push for cutting-edge technology innovation. Many of these novel technologies built for the spaceflight environment also have numerous benefits for human health on Earth. In this manuscript, we focus on "Spaceflight-to-Eye Clinic" and discuss technologies built for the spaceflight environment that eventually helped to optimize ophthalmic health on Earth (e.g., LADAR for satellite docking now utilized in eye-tracking technology for LASIK). We also discuss current technology research for spaceflight associated neuro-ocular syndrome (SANS) that may also be applied to terrestrial ophthalmic health. Ultimately, various advances made to enable to the future of space exploration have also advanced the ophthalmic health of individuals on Earth.
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Affiliation(s)
- Joshua Ong
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI, United States.
| | - Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom
| | - Mouayad Masalkhi
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Alex Suh
- Tulane University School of Medicine, New Orleans, LA, United States
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Phani Paladugu
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, United States; University of Texas MD Anderson Cancer Center, Houston, TX, United States; Texas A&M College of Medicine, TX, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, United States
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Masalkhi M, Ong J, Waisberg E, Lee AG. Deep learning in ophthalmic and orbital ultrasound for spaceflight associated neuro-ocular syndrome (SANS). Eye (Lond) 2024; 38:1397. [PMID: 38135772 PMCID: PMC11076631 DOI: 10.1038/s41433-023-02877-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/14/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Affiliation(s)
- Mouayad Masalkhi
- University College Dublin School of Medicine, Belfield, Dublin, Ireland.
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom
- Moorfields Eye Hospital, NHS Foundation Trust, London, United Kingdom
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Bryan, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
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Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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Kamran SA, Hossain KF, Ong J, Zaman N, Waisberg E, Paladugu P, Lee AG, Tavakkoli A. SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data. NPJ Microgravity 2024; 10:40. [PMID: 38548790 PMCID: PMC10978911 DOI: 10.1038/s41526-024-00364-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 02/12/2024] [Indexed: 04/01/2024] Open
Abstract
Spaceflight associated neuro-ocular syndrome (SANS) is one of the largest physiologic barriers to spaceflight and requires evaluation and mitigation for future planetary missions. As the spaceflight environment is a clinically limited environment, the purpose of this research is to provide automated, early detection and prognosis of SANS with a machine learning model trained and validated on astronaut SANS optical coherence tomography (OCT) images. In this study, we present a lightweight convolutional neural network (CNN) incorporating an EfficientNet encoder for detecting SANS from OCT images titled "SANS-CNN." We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing with a combination of terrestrial images and astronaut SANS images for both testing and validation. SANS-CNN was validated with SANS images labeled by NASA to evaluate accuracy, specificity, and sensitivity. To evaluate real-world outcomes, two state-of-the-art pre-trained architectures were also employed on this dataset. We use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of SANS-CNN's prediction. SANS-CNN achieved 84.2% accuracy on the test set with an 85.6% specificity, 82.8% sensitivity, and 84.1% F1-score. Moreover, SANS-CNN outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, in accuracy by 21.4% and 13.1%, respectively. We also apply two class-activation map techniques to visualize critical SANS features perceived by the model. SANS-CNN represents a CNN model trained and validated with real astronaut OCT images, enabling fast and efficient prediction of SANS-like conditions for spaceflight missions beyond Earth's orbit in which clinical and computational resources are extremely limited.
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Affiliation(s)
- Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, US
| | - Khondker Fariha Hossain
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, US
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, US
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, US
| | - Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, UK
| | - Phani Paladugu
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, US
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, US
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, US
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, US
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, US
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, US
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, US
- University of Texas MD Anderson Cancer Center, Houston, TX, US
- Texas A&M College of Medicine, Bryan, TX, US
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, US
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, NV, US.
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Waisberg E, Ong J, Masalkhi M, Zaman N, Kamran SA, Sarker P, Lee AG, Tavakkoli A. ChatGPT and medical education: a new frontier for emerging physicians. CANADIAN MEDICAL EDUCATION JOURNAL 2023; 14:128-130. [PMID: 38226297 PMCID: PMC10787866 DOI: 10.36834/cmej.77644] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom
- Moorfields Eye Hospital, NHS Foundation Trust, London, United Kingdom
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Michigan, USA
| | - Mouayad Masalkhi
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Nevada, USA
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Nevada, USA
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Nevada, USA
| | - Andrew G Lee
- The Houston Methodist Research Institute, Houston Methodist Hospital, Texas, USA
- Center for Space Medicine, Baylor College of Medicine, Texas, USA
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Nevada, USA
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Waisberg E, Ong J, Masalkhi M, Zaman N, Kamran SA, Sarker P, Lee AG, Tavakkoli A. Generative Pre-Trained Transformers (GPT) and Space Health: A Potential Frontier in Astronaut Health During Exploration Missions. Prehosp Disaster Med 2023; 38:532-536. [PMID: 37264946 PMCID: PMC10445113 DOI: 10.1017/s1049023x23005848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 04/29/2023] [Indexed: 06/03/2023]
Abstract
In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.
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Affiliation(s)
- Ethan Waisberg
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, MichiganUSA
| | - Mouayad Masalkhi
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada - Reno, Reno, NevadaUSA
| | - Sharif Amit Kamran
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada - Reno, Reno, NevadaUSA
| | - Prithul Sarker
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada - Reno, Reno, NevadaUSA
| | - Andrew G. Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TexasUSA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TexasUSA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TexasUSA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New YorkUSA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TexasUSA
- University of Texas MD Anderson Cancer Center, Houston, TexasUSA
- Texas A&M College of Medicine, Bryan, TexasUSA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IowaUSA
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada - Reno, Reno, NevadaUSA
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