Operative Digital Enhancement of Macular Pigment during Macular Surgery

Sandali, Otman; Hassani, Rachid Tahiri Joutei; Balamoun, Ashraf Armia; Franklin, Alan J; Sallam, Ahmed; Borderie, Vincent

doi:10.3390/jcm12062300

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…internal limiting membrane peeling, making macular pigment more visible during macular surgery, and image-sharpening algorithms to improve the intraoperative visibility of 3D heads-up surgery. [10][11][12] However, the modi ed images obtained from the aforementioned studies involved the application of uniform changes to all images. Given the differences in the structure and color of the retina among individuals, surgical accuracy and e ciency would increase if the surgery can be performed using optimized images tailored to each eye.…”

Section: Introductionmentioning

confidence: 99%

Digital image enhancement using Deep learning algorithm in 3D heads-up vitreoretinal surgery

Hwang,

Cho,

Kim

et al. 2024

Preprint

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This study aims to predict the optimal imaging parameters using a deep learning algorithm in 3D heads-up vitreoretinal surgery and assess its effectiveness on improving the vitreoretinal surface visibility during surgery. To develop the deep learning algorithm, we utilized 212 manually-optimized still images extracted from epiretinal membrane (ERM) surgical videos. These images were applied to a two-stage Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) architecture. The algorithm’s performance was evaluated based on the peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM), and the degree of surgical image enhancement by the algorithm was evaluated based on sharpness, brightness, and contrast values. A survey was conducted to evaluate the intraoperative suitability of optimized images. For an in-vitro experiment, 121 anonymized high-resolution ERM fundus images were optimized using a 3D display based on the algorithm. The PSNR and SSIM values are 34.59 ± 5.34 and 0.88 ± 0.08, respectively. The algorithm enhances the sharpness, brightness and contrast values of the surgical images. In the in-vitro experiment, both the ERM size and color contrast ratio increased significantly in the optimized fundus images. Both surgical and fundus images are digitally enhanced using a deep learning algorithm. Digital image enhancement using this algorithm can be potentially applied to 3D heads-up vitreoretinal surgeries.

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Section: Introductionmentioning

confidence: 99%

Digital image enhancement using Deep learning algorithm in 3D heads-up vitreoretinal surgery

Hwang,

Cho,

Kim

et al. 2024

Preprint

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Use of Color Channel Optimization in 3D Heads-Up Vitrectomy vs Standard Operating Microscope for Macular Surgeries

Agarwal,

Ghazi,

El Ghrably

et al. 2024

Journal of VitreoRetinal Diseases

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Purpose: To compare the efficacy and safety of color channel optimization with 3-dimensional (3D) heads-up vitrectomy (3D HUD group) vs standard operating microscope vitrectomy (control group) for macular surgery. Methods: This retrospective multicenter comparative study comprised patients having 25-gauge pars plana vitrectomy for macular hole, epiretinal membrane (ERM), or vitreomacular traction. The minimum follow-up was 6 months. Surgeons completed a subjective questionnaire after each case. The main outcome measures were safety related (dye reinjection rate, macular ERM or internal limiting membrane [ILM] peeling time, endoillumination intensity). Other outcome measures included total surgical time, surgical outcomes, and subjective surgeon-related parameters. Results: The study included 74 eyes (36 in 3D HUD group; 38 in control group). There were no statistical differences in baseline parameters between groups. Significantly more eyes in the control group than in the 3D HUD group required dye reinjection (23.7% vs 5.6%; P = .03). Less time was required for ERM and ILM peeling in the 3D HUD group (both P < .01); however, the total surgical time was the same between groups. Eyes in the 3D HUD group required lower endoillumination ( P < .001). There were no between-group differences in the rates of complications. Surgeons said depth perception was better in the control group (P < .001), with no differences in comfort or visibility. Conclusions: 3D heads-up–based color channel optimization for macular surgeries is safe and effective. Although it may have safety advantages, it did not affect the visual or anatomic outcomes or total surgical time and did not improve surgeon comfort or visibility.

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Evaluating the Effect of Image Enhancement on Diagnostic Reliability in Dry Eye Disease Using a Portable Imaging Device

Mizukami,

Sato,

Asai

et al. 2024

Diagnostics

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Background: This study aimed to evaluate the impact of image enhancement techniques on the interobserver reliability of tear break-up time (TBUT), tear meniscus height (TMH), corneal fluorescein staining (CFS) scoring, and conjunctivochalasis detection using the Smart Eye Camera (SEC), a portable device for anterior segment examination. Methods: A retrospective analysis was conducted on video recordings captured by the SEC from 46 patients with dry eye disease (DED). Separate sets of images were created for each level of enhancement: unenhanced (G0), mildly enhanced (G3), and strongly enhanced (G7). These sets were not intermixed, ensuring that each enhancement level was assessed independently. Three observers—two DED specialists and one general ophthalmologist—assessed TBUT, TMH, CFS scores, and conjunctivochalasis. Interobserver reliability was evaluated using intraclass correlation coefficients (ICCs) for each image set. Results: Interobserver reliability for CFS scores significantly improved with G3, yielding an ICC of 0.8413. In contrast, G7 improved reliability for TBUT measurements (ICC = 0.7381), but led to a notable decrease in reliability for both CFS scoring (ICC = 0.2259) and conjunctivochalasis detection (ICC = 0.0786). Furthermore, the assessment of TMH demonstrated a progressive decline in accuracy with increasing levels of image enhancement. Conclusions: Image enhancement using the SEC improved the diagnostic consistency of dry eye specialists and general ophthalmologists, especially for TBUT and CFS assessments. However, excessive enhancement may obscure key diagnostic features, indicating the need for careful optimization of image processing techniques depending on the diagnostic focus.

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Operative Digital Enhancement of Macular Pigment during Macular Surgery

Cited by 3 publications

References 19 publications

Digital image enhancement using Deep learning algorithm in 3D heads-up vitreoretinal surgery

Digital image enhancement using Deep learning algorithm in 3D heads-up vitreoretinal surgery

Use of Color Channel Optimization in 3D Heads-Up Vitrectomy vs Standard Operating Microscope for Macular Surgeries

Evaluating the Effect of Image Enhancement on Diagnostic Reliability in Dry Eye Disease Using a Portable Imaging Device

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