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ngenuity 3d visualization system  (alcon inc)

 
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    alcon inc ngenuity 3d visualization system
    Schematic diagram of digital image enhancement using deep learning algorithm. The vitreoretinal surgeon manually adjusted eight image parameters (brightness, saturation, contrast, hue, gamma, cyan, magenta, and yellow) to optimize the original surgical image and placed the adjusted image and parameter values in the algorithm architecture to train the deep learning model. After training the deep learning model, eight image parameter values for optimization were predicted when the original surgical image was applied to the algorithm. By inputting the predicted parameter values into the <t>Ngenuity</t> <t>3D</t> Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.
    Ngenuity 3d Visualization System, supplied by alcon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/ngenuity+3d+visualization+system/ngenuity+3d+visualization+system/pmc12106625-36-8-12
    Average 90 stars, based on 1 article reviews
    ngenuity 3d visualization system - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery"

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery

    Journal: Scientific Reports

    doi: 10.1038/s41598-025-98801-7

    Schematic diagram of digital image enhancement using deep learning algorithm. The vitreoretinal surgeon manually adjusted eight image parameters (brightness, saturation, contrast, hue, gamma, cyan, magenta, and yellow) to optimize the original surgical image and placed the adjusted image and parameter values in the algorithm architecture to train the deep learning model. After training the deep learning model, eight image parameter values for optimization were predicted when the original surgical image was applied to the algorithm. By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.
    Figure Legend Snippet: Schematic diagram of digital image enhancement using deep learning algorithm. The vitreoretinal surgeon manually adjusted eight image parameters (brightness, saturation, contrast, hue, gamma, cyan, magenta, and yellow) to optimize the original surgical image and placed the adjusted image and parameter values in the algorithm architecture to train the deep learning model. After training the deep learning model, eight image parameter values for optimization were predicted when the original surgical image was applied to the algorithm. By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Techniques Used:

    In-vitro experiments for evaluation of optimized fundus image using Ngenuity 3D visualization system. ( A , B ) 121 high-resolution fundus images of epiretinal membrane (ERM) obtained from Google images and a dataset. The fundus images of ERM obtained were directly observed in a 3D heads-up surgery setting. ( C ) Manual manipulation of scroll bars within the system. An optimized fundus image was obtained by adjusting the image parameter values. ( D – F ) Pelli–Robson contrast sensitivity chart. This chart was printed in a small size and directly observed in the 3D heads-up surgery setting by 25 investigators. Subsequently, it was optimized, and the number of letters that can be read before and after optimization was compared.
    Figure Legend Snippet: In-vitro experiments for evaluation of optimized fundus image using Ngenuity 3D visualization system. ( A , B ) 121 high-resolution fundus images of epiretinal membrane (ERM) obtained from Google images and a dataset. The fundus images of ERM obtained were directly observed in a 3D heads-up surgery setting. ( C ) Manual manipulation of scroll bars within the system. An optimized fundus image was obtained by adjusting the image parameter values. ( D – F ) Pelli–Robson contrast sensitivity chart. This chart was printed in a small size and directly observed in the 3D heads-up surgery setting by 25 investigators. Subsequently, it was optimized, and the number of letters that can be read before and after optimization was compared.

    Techniques Used: In Vitro, Membrane

    Measurements of color contrast ratio (CCR) and epiretinal membrane (ERM) area in experimental fundus images on Ngenuity 3D visualization system. ( A , B ) On the original fundus image of ERM, the CCR was measured by comparing the red, green, and blue values between the darkest pixel (number 1 on C) and the brightest pixel (number 2 on C) on the prominent retinal folds. Based on this original fundus image, the CCR was measured to be 1.17. ( C , D ) The optimized fundus image was measured in the same manner as the original fundus image, and the CCR value calculated using this method was 2.12. This value is higher than that of the original fundus image, which implies that the folds of the ERM can be observed more clearly; in other words, visibility is improved on the optimized fundus image. ( E , F ) The same ERM was measured to be 19,777 and 22,581 pixels in the original and optimized fundus images, respectively, although the optimized fundus image was measured more extensively.
    Figure Legend Snippet: Measurements of color contrast ratio (CCR) and epiretinal membrane (ERM) area in experimental fundus images on Ngenuity 3D visualization system. ( A , B ) On the original fundus image of ERM, the CCR was measured by comparing the red, green, and blue values between the darkest pixel (number 1 on C) and the brightest pixel (number 2 on C) on the prominent retinal folds. Based on this original fundus image, the CCR was measured to be 1.17. ( C , D ) The optimized fundus image was measured in the same manner as the original fundus image, and the CCR value calculated using this method was 2.12. This value is higher than that of the original fundus image, which implies that the folds of the ERM can be observed more clearly; in other words, visibility is improved on the optimized fundus image. ( E , F ) The same ERM was measured to be 19,777 and 22,581 pixels in the original and optimized fundus images, respectively, although the optimized fundus image was measured more extensively.

    Techniques Used: Membrane

    Related Articles

    Membrane:

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery
    Article Snippet: .. Surgical videos of the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) of patients who were diagnosed of epiretinal membrane and underwent vitrectomy at a single center of a university hospital between July 1, 2020 and May 31, 2022 were reviewed through TrueWare software (version 9.5.4). ..

    Article Title: Increasing the Visibility During Internal Limiting Membrane Peeling in Vitrectomy Surgery: Evaluation of Monochrome Mode with Brilliant Blue G Staining Using the NGENUITY 3D Visualization System®
    Article Snippet: Purpose: The aim of the study is to explore the effectiveness of using monochrome mode with Brilliant Blue G during vitrectomy surgery with the NGENUITY 3D Visualization System.. Methods: Vitrectomy cases involving BBG-stained internal limiting membrane peeling with the NGENUITY 3D Visualization System (Alcon Laboratories, Inc.) at Juntendo University Urayasu Hospital from October 2022 to February 2023 were selected.. The contrast ratios between peeled and stained internal limiting membrane areas under three settings were assessed: normal, yellow, and monochrome.

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery.
    Article Snippet: .. Surgical images: data preparation and evaluation Surgical videos of the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) of patients who were diagnosed of epiretinal membrane and underwent vitrectomy at a single center of a university hospital between July 1, 2020 and May 31, 2022 were reviewed through TrueWare software (version 9.5.4). ..

    Article Title: Real-Time Integration of Optical Coherence Tomography Thickness Map Overlays for Enhanced Visualization in Epiretinal Membrane Surgery: A Pilot Study
    Article Snippet: .. While modern surgical microscopes, such as the ARTEVO 800 Digital Microscope (Carl Zeiss Meditec AG, Jena, Germany) and the Ngenuity 3D Visualization System (Alcon Laboratories Inc., Fort Worth, TX, USA), integrate OCT B-scans into the surgical field, the instruments themselves, such as forceps used for MP, may block the visual signal causing shadows that obscure or distort the visualization of the membrane at critical moments [ , ]. ..

    Software:

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery
    Article Snippet: .. Surgical videos of the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) of patients who were diagnosed of epiretinal membrane and underwent vitrectomy at a single center of a university hospital between July 1, 2020 and May 31, 2022 were reviewed through TrueWare software (version 9.5.4). ..

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery.
    Article Snippet: .. Surgical images: data preparation and evaluation Surgical videos of the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) of patients who were diagnosed of epiretinal membrane and underwent vitrectomy at a single center of a university hospital between July 1, 2020 and May 31, 2022 were reviewed through TrueWare software (version 9.5.4). ..

    other:

    Article Title: Optical coherence tomography guided robotic ophthalmic procedures
    Article Snippet: The display 108 and the external display 110 may be part of an ophthalmic visualization system that provides a platform for digitally assisted ophthalmic procedures, such as NGENUITY 3D Visualization system of Alcon Laboratories Inc.

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery
    Article Snippet: By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Microscopy:

    Article Title: Light as a drug: prospective randomized evaluation and comparison of the effect of decreased illumination on visual recovery after cataract surgery
    Article Snippet: This study represents a prospective, randomized study of 214 consecutive eyes of 132 patients who underwent cataract surgery between January 2021 and January 2022 at the Long Island Ambulatory Surgery Center in Brentwood, NY. .. Patients were randomized using a random number generator ( www.calculatorsoup.com ) on the day of surgery and grouped into 2 surgical visualization approaches: (1) use of only the OPMI Lumera T operating microscope (Carl Zeiss Meditech AG) (“traditional group”) or (2) use of the NGENUITY 3D Visualization System (Alcon Laboratories, Inc.) affixed to the OPMI Lumera T microscope (“digital group”) (Figure ). ..

    Article Title: Real-Time Integration of Optical Coherence Tomography Thickness Map Overlays for Enhanced Visualization in Epiretinal Membrane Surgery: A Pilot Study
    Article Snippet: .. While modern surgical microscopes, such as the ARTEVO 800 Digital Microscope (Carl Zeiss Meditec AG, Jena, Germany) and the Ngenuity 3D Visualization System (Alcon Laboratories Inc., Fort Worth, TX, USA), integrate OCT B-scans into the surgical field, the instruments themselves, such as forceps used for MP, may block the visual signal causing shadows that obscure or distort the visualization of the membrane at critical moments [ , ]. ..

    Blocking Assay:

    Article Title: Real-Time Integration of Optical Coherence Tomography Thickness Map Overlays for Enhanced Visualization in Epiretinal Membrane Surgery: A Pilot Study
    Article Snippet: .. While modern surgical microscopes, such as the ARTEVO 800 Digital Microscope (Carl Zeiss Meditec AG, Jena, Germany) and the Ngenuity 3D Visualization System (Alcon Laboratories Inc., Fort Worth, TX, USA), integrate OCT B-scans into the surgical field, the instruments themselves, such as forceps used for MP, may block the visual signal causing shadows that obscure or distort the visualization of the membrane at critical moments [ , ]. ..



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    Image Search Results


    Schematic diagram of digital image enhancement using deep learning algorithm. The vitreoretinal surgeon manually adjusted eight image parameters (brightness, saturation, contrast, hue, gamma, cyan, magenta, and yellow) to optimize the original surgical image and placed the adjusted image and parameter values in the algorithm architecture to train the deep learning model. After training the deep learning model, eight image parameter values for optimization were predicted when the original surgical image was applied to the algorithm. By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Journal: Scientific Reports

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery

    doi: 10.1038/s41598-025-98801-7

    Figure Lengend Snippet: Schematic diagram of digital image enhancement using deep learning algorithm. The vitreoretinal surgeon manually adjusted eight image parameters (brightness, saturation, contrast, hue, gamma, cyan, magenta, and yellow) to optimize the original surgical image and placed the adjusted image and parameter values in the algorithm architecture to train the deep learning model. After training the deep learning model, eight image parameter values for optimization were predicted when the original surgical image was applied to the algorithm. By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Article Snippet: By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Techniques:

    In-vitro experiments for evaluation of optimized fundus image using Ngenuity 3D visualization system. ( A , B ) 121 high-resolution fundus images of epiretinal membrane (ERM) obtained from Google images and a dataset. The fundus images of ERM obtained were directly observed in a 3D heads-up surgery setting. ( C ) Manual manipulation of scroll bars within the system. An optimized fundus image was obtained by adjusting the image parameter values. ( D – F ) Pelli–Robson contrast sensitivity chart. This chart was printed in a small size and directly observed in the 3D heads-up surgery setting by 25 investigators. Subsequently, it was optimized, and the number of letters that can be read before and after optimization was compared.

    Journal: Scientific Reports

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery

    doi: 10.1038/s41598-025-98801-7

    Figure Lengend Snippet: In-vitro experiments for evaluation of optimized fundus image using Ngenuity 3D visualization system. ( A , B ) 121 high-resolution fundus images of epiretinal membrane (ERM) obtained from Google images and a dataset. The fundus images of ERM obtained were directly observed in a 3D heads-up surgery setting. ( C ) Manual manipulation of scroll bars within the system. An optimized fundus image was obtained by adjusting the image parameter values. ( D – F ) Pelli–Robson contrast sensitivity chart. This chart was printed in a small size and directly observed in the 3D heads-up surgery setting by 25 investigators. Subsequently, it was optimized, and the number of letters that can be read before and after optimization was compared.

    Article Snippet: By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Techniques: In Vitro, Membrane

    Measurements of color contrast ratio (CCR) and epiretinal membrane (ERM) area in experimental fundus images on Ngenuity 3D visualization system. ( A , B ) On the original fundus image of ERM, the CCR was measured by comparing the red, green, and blue values between the darkest pixel (number 1 on C) and the brightest pixel (number 2 on C) on the prominent retinal folds. Based on this original fundus image, the CCR was measured to be 1.17. ( C , D ) The optimized fundus image was measured in the same manner as the original fundus image, and the CCR value calculated using this method was 2.12. This value is higher than that of the original fundus image, which implies that the folds of the ERM can be observed more clearly; in other words, visibility is improved on the optimized fundus image. ( E , F ) The same ERM was measured to be 19,777 and 22,581 pixels in the original and optimized fundus images, respectively, although the optimized fundus image was measured more extensively.

    Journal: Scientific Reports

    Article Title: Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery

    doi: 10.1038/s41598-025-98801-7

    Figure Lengend Snippet: Measurements of color contrast ratio (CCR) and epiretinal membrane (ERM) area in experimental fundus images on Ngenuity 3D visualization system. ( A , B ) On the original fundus image of ERM, the CCR was measured by comparing the red, green, and blue values between the darkest pixel (number 1 on C) and the brightest pixel (number 2 on C) on the prominent retinal folds. Based on this original fundus image, the CCR was measured to be 1.17. ( C , D ) The optimized fundus image was measured in the same manner as the original fundus image, and the CCR value calculated using this method was 2.12. This value is higher than that of the original fundus image, which implies that the folds of the ERM can be observed more clearly; in other words, visibility is improved on the optimized fundus image. ( E , F ) The same ERM was measured to be 19,777 and 22,581 pixels in the original and optimized fundus images, respectively, although the optimized fundus image was measured more extensively.

    Article Snippet: By inputting the predicted parameter values into the Ngenuity 3D Visualization System (Alcon Laboratories, Fort Worth, TX) capable of digital image enhancement, an optimized surgical image was finally obtained.

    Techniques: Membrane