Wavefront sensor-less adaptive optics using deep reinforcement learning

Durech, Eduard; Newberry, William; Franke, Jonas; Sarunic, Marinko V.

doi:10.1364/boe.427970

Cited by 20 publications

(9 citation statements)

References 27 publications

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“…To design a ML algorithm working on images other than PSFs, Ref. [ 186 ] proposed a reinforcement learning (RL) based algorithm and demonstrated it on fiber-like tissue phantoms. However, the method still involved iterative correction and was not shown to be more efficient or effective than many other non-ML driven sensorless AO algorithms.…”

Section: Machine-learning and Computational Approachesmentioning

confidence: 99%

Adaptive optics for optical microscopy [Invited]

Zhang¹,

Berlage

et al. 2023

Biomed. Opt. Express

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Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy.

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Section: Machine-learning and Computational Approachesmentioning

confidence: 99%

Adaptive optics for optical microscopy [Invited]

Zhang¹,

Berlage

et al. 2023

Biomed. Opt. Express

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“…In addition, the recent development of novel imaging techniques has resulted in huge opportunities for data-driven AI approaches for basic science studies. For example, the recent advancements of adaptive optics, which is adopted from telescope technology, and two-photon imaging (94)(95)(96) have enabled in vivo visualization of glaucoma-related ocular structures such as the retina, ONH and trabecular meshwork in unprecedented detail (97). These data can facilitate efficient and accurate retinal layer segmentations (98,99), cellular-level imaging of photoreceptors (100), and detect subtle pathological protein deposition in RNFL in both human (101) and animal studies (102).…”

Section: Future Research Directionsmentioning

confidence: 99%

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Pasquale

Girard

et al. 2023

Front. Ophthalmol.

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Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

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“…Sensorless AO (SAO) is another alternative to hardware-based AO-OCT that relies on the images’ properties rather than a wavefront sensor to ultimately measure and correct aberrations [ 56 ]. SAO optimization methods and algorithms include Zernike Mode Hill Climbing [ 57 ], stochastic parallel gradient descent [ 58 , 59 ], deep reinforcement learning [ 60 ], and others. SAO features have been tested to some extent in CAO models as well [ 58 , 61 ].…”

Section: Adaptive Optics (Ao) In Oct (Ao-oct)mentioning

confidence: 99%

Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders

Ong

Zarnegar

Corradetti

et al. 2022

JCM

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Optical coherence tomography (OCT) imaging has played a pivotal role in the field of retina. This light-based, non-invasive imaging modality provides high-quality, cross-sectional analysis of the retina and has revolutionized the diagnosis and management of retinal and choroidal diseases. Since its introduction in the early 1990s, OCT technology has continued to advance to provide quicker acquisition times and higher resolution. In this manuscript, we discuss some of the most recent advances in OCT technology and techniques for choroidal and retinal diseases. The emerging innovations discussed include wide-field OCT, adaptive optics OCT, polarization sensitive OCT, full-field OCT, hand-held OCT, intraoperative OCT, at-home OCT, and more. The applications of these rising OCT systems and techniques will allow for a closer monitoring of chorioretinal diseases and treatment response, more robust analysis in basic science research, and further insights into surgical management. In addition, these innovations to optimize visualization of the choroid and retina offer a promising future for advancing our understanding of the pathophysiology of chorioretinal diseases.

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Wavefront sensor-less adaptive optics using deep reinforcement learning

Cited by 20 publications

References 27 publications

Adaptive optics for optical microscopy [Invited]

Adaptive optics for optical microscopy [Invited]

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders

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