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Cataract surgery education requires balancing surgical training with patient safety, yet the precise phases of surgical skill development remain unclear. Here we analyze 4255 cataract surgeries performed by 15 novice surgeons to identify distinct phases in the learning curve based on complication rates. Using a custom smoothing filter and the PELT (Pruned Exact Linear Time) algorithm, we identified four distinct phases: Phase 1 (cases 1–87) with a complication rate of 3.18% (95% CI 3.13–3.24%), Phase 2 (cases 88–189) with 1.68% (95% CI 1.52–1.85%), Phase 3 (cases 190–534) with 0.79% (95% CI 0.77–0.81%), and Phase 4 (cases 535–711) with 0.18% (95% CI 0.16–0.20%). The experience speed stabilized after approximately 123 cases, and novice surgeons required 535 cases to achieve complication rates comparable to experienced surgeons (0.53%). Notably, surgeons with less than 535 cases accounted for 40.9% of all complications in our educational setting. The overall complication rate for the first 50 cases was 3.1%, lower than previously reported international rates (4.8–11.6%). These findings provide statistical evidence for the stages of surgical proficiency development and suggest that structured training programs should be tailored to these distinct learning phases to optimize surgical outcomes and patient safety.

Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep learning, a machine-learning technology, to detect RRD using ultra–wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2–100%] and a high specificity of 96.5% (95% CI, 90.2–100%), and the area under the curve was 0.988 (95% CI, 0.981–0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra–wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (CCC), nuclear extraction, and three other surgical phases of cataract surgery using artificial intelligence technology. A total of 303 cases of cataract surgery registered in the clinical database of the Ophthalmology Department of Tsukazaki Hospital were used as a dataset. Surgical videos were downsampled to a resolution of 299 × 168 at 1 FPS to image each frame. Next, based on the start and end times of each surgical phase recorded by an ophthalmologist, the obtained images were labeled correctly. Using the data, a neural network model, known as InceptionV3, was developed to identify the given surgical phase for each image. Then, the obtained images were processed in chronological order using the neural network model, where the moving average of the output result of five consecutive images was derived. The class with the maximum output value was defined as the surgical phase. For each surgical phase, the time at which a phase was first identified was defined as the start time, and the time at which a phase was last identified was defined as the end time. The performance was evaluated by finding the mean absolute error between the start and end times of each important phase recorded by the ophthalmologist as well as the start and end times determined by the model. The correct response rate of the cataract surgical phase classification was 90.7% for CCC, 94.5% for nuclear extraction, and 97.9% for other phases, with a mean correct response rate of 96.5%. The errors between each phase’s start and end times recorded by the ophthalmologist and those determined by the neural network model were as follows: CCC’s start and end times, 3.34 seconds and 4.43 seconds, respectively and nuclear extraction’s start and end times, 7.21 seconds and 6.04 seconds, respectively, with a mean of 5.25 seconds. The neural network model used in this study was able to perform the classification of the surgical phase by only referring to the last 5 seconds of video images. Therefore, our method has performed like a real-time classification.

To compare the visual performance of a monofocal intraocular lens (IOL) (ZCB00) and a multifocal IOL (ZMB00) of the same material and basic design, we evaluated postoperative parameters at 10 weeks after the last surgery in cataract patients who underwent bilateral ZCB00 or ZMB00 implantation from December 13, 2010, to July 29, 2019, with the right and left lenses implanted within 3 months of each other. The study enrolled 2,230 eyes of 1,115 patients. The monofocal group comprised 904 eyes of 452 patients (72.3 ± 6.8 years; females/males, 268/184), and the multifocal group comprised 1,326 eyes of 663 patients (67.0 ± 7.8 years; females/males, 518/145). Contrast sensitivity (4.0/2.5/1.6/1.0/0.7 degrees), contrast sensitivity with glare (1.6/1.0/0.7 degrees), and the VFQ-25 score for driving at night were significantly better in the monofocal group (p < 0.00068, Wald test). Uncorrected intermediate/near visual acuity and near spectacle independence were significantly better in the multifocal group (p < 0.00068, Wald test). The two IOL groups had different characteristics in terms of contrast sensitivity, night-time driving, uncorrected intermediate/near visual acuity and near spectacle independence.

The cervical ossification of the posterior longitudinal ligament (cOPLL) is sometimes misdiagnosed or overlooked on radiography. Thus, this study aimed to validate the diagnostic yield of our deep learning algorithm which diagnose the presence/absence of cOPLL on cervical radiography and highlighted areas of ossification in positive cases and compare its diagnostic accuracy with that of experienced spine physicians. Firstly, the radiographic data of 486 patients (243 patients with cOPLL and 243 age and sex matched controls) who received cervical radiography and a computer tomography were used to create the deep learning algorithm. The diagnostic accuracy of our algorithm was 0.88 (area under curve, 0.94). Secondly, the numbers of correct diagnoses were compared between the algorithm and consensus of four spine physicians using 50 independent samples. The algorithm had significantly more correct diagnoses than spine physicians (47/50 versus 39/50, respectively; p  = 0.041). In conclusion , the accuracy of our deep learning algorithm for cOPLL diagnosis was significantly higher than that of experienced spine physicians. We believe our algorithm, which uses different diagnostic criteria than humans, can significantly improve the diagnostic accuracy of cOPLL when radiography is used.

We compared the visual performance of a monofocal intraocular lens (IOL) (ZCB00) and a rotationally asymmetric refractive IOL with +1.5 diopters near addition (Lentis Comfort LS-313 MF15) by investigating the ten-week postoperative parameters of both eyes of cataract patients who underwent bilateral implantation with one of the two IOLs within three months between 2011 and 2019. A total of 1352 eyes of 676 patients were enrolled; the ZCB00 group comprised 904 eyes of 452 patients (72.3 ± 6.8 years; females/males, 268/184), and the LS-313 MF15 group included 448 eyes of 224 patients (73.6 ± 7.0 years; females/males, 125/99). Comparisons were made with a linear mixed-effects model, strictly adjusting for sex, age, subjective refraction spherical equivalent, subjective refraction cylinder, corneal astigmatism, axial length, corneal higher-order aberrations, and pupil diameter to ensure statistical validity. The corrected distance visual acuity was significantly better, and most of the higher-order aberrations were significantly smaller in the ZCB00 group (p < 0.00068, Wald test). Contrast sensitivity with glare (6.3 degrees) and spectacle independence (near) were significantly better in the LS-313 MF15 group (p < 0.00068, Wald test).

We compared the visual performance of ZCB00 ultraviolet light-filtering and ZCB00V violet light-filtering monofocal intraocular lenses (IOLs) (both Johnson & Johnson Surgical Vision) with the same materials and basic design in cataract patients treated from 2011 to 2020. The evaluations were performed 10 weeks after the last surgery for implantation of bilateral lenses ≤ 3 months apart. The ZCB00 and ZCB00V groups included 904 eyes from 452 patients (age 72.3 ± 6.8 y; women/men, 268/184) and 1374 eyes from 687 patients (age 73.0 ± 7.4 y; women/men, 415/272), respectively. Statistical validity was confirmed using a linear mixed-effects model with binocular data and adjustments for age, sex, subjective refraction cylinder, subjective refraction spherical equivalent, corneal astigmatism, axial length, pupil diameter, and corneal higher-order aberrations. ZCB00 showed slightly but significantly better results ( p  < 0.05, Wald) for uncorrected intermediate/near visual acuity, corrected near visual acuity, and components of the 25-item Visual Function Questionnaire (VFQ-25) (Role_Limitation, Mental_Health, Social_Function, Distance_Vision, Color_Vision). Additionally, ZCB00V showed significantly better contrast sensitivity with glare (visual angle of target: 6.3°/4.0°/0.7°; p  < 0.00068, Wald); slightly but significantly better contrast sensitivity without glare (4.0°/2.5°/1.6°) and with glare (2.5°/1.6°/1.0°), VFQ-25 General_Health scores, and near spectacle independence; and slightly but significantly smaller higher-order aberrations (internal, scaled to a 6-mm pupil; Wavefront_6_post_I_Trefoil) ( p  < 0.05, Wald).

To evaluate the visual performance of a diffractive bifocal intraocular lens (IOL) with + 4.0 D near addition (ZMB00) and a diffractive trifocal IOL with + 2.17 D and + 3.25 D near addition (AcrySof IQ PanOptix TFNT00), we investigated the 10-week postoperative parameters after cataract surgery in which ZMB00 or TFNT00 lenses were implanted bilaterally from 2011 to 2020 (with a 3-month interval between implantation of the right and left lenses). The study included 1448 eyes of 724 patients. The diffractive bifocal group comprised 1326 eyes of 663 patients (aged 67.0 ± 7.8 years; females/males, 518/145), and the diffractive trifocal group comprised 122 eyes of 61 patients (aged 66.6 ± 7.3 years; females/males, 35/26). A linear mixed-effects model using data for both eyes, with strict adjustments for sex, age, subjective refraction spherical equivalent, subjective refraction cylinder, corneal astigmatism, axial length, corneal higher-order aberrations, and pupil diameter, ensured statistical validity. Uncorrected near visual acuity and higher-order aberrations (ocular/internal, scaled to a pupil size of 4 mm) (Wavefront_4mm_postoperative_Ocular/Internal_Spherical) were significantly better in the bifocal group ( p  < 0.00068, Wald test). Uncorrected intermediate visual acuity, contrast sensitivity (6.3/4.0/2.5/1.6/1.0/0.7 degrees), and contrast sensitivity with glare (4.0/1.6/1.0/0.7 degrees) were significantly better in the trifocal group ( p  < 0.00068, Wald test).

We compared the visual performance of a bifocal intraocular lens (IOL) (ZMB00) and an extended-depth-of-focus (EDOF) IOL (ZXR00V) by evaluating postoperative parameters at 10 weeks after the last surgery in cataract patients who underwent bilateral ZMB00 or ZXR00V implantation between 2011 and 2020. The right and left lenses were implanted within 3 months of each other. The study enrolled 1536 eyes of 768 patients; the ZMB00 group comprised 1326 eyes of 663 patients (age: 67.0 ± 7.8 years; female/male, 518/145), and the ZXR00V group comprised 210 eyes of 105 patients (age: 67.8 ± 6.9 years; female/male, 39/66). A linear mixed-effects model using data for both eyes, with strict adjustments for sex, age, subjective refraction spherical equivalent, subjective refraction cylinder, corneal astigmatism, axial length, corneal higher-order aberrations and pupil diameter, ensured statistical validity. Uncorrected near visual acuity, corrected near visual acuity, and near spectacle independence were significantly better in the ZMB00 group (p<0.00068, Wald test) than in the ZXR00V group. Contrast sensitivity (visual angle of the test target: 4.0°/2.5°/1.6°/1.0°/0.7°) and contrast sensitivity with glare (4.0°/2.5°/1.6°/1.0°/0.7°) were significantly better in the ZXR00V group (p<0.00068, Wald test) than in the ZMB00 group. Uncorrected intermediate visual acuity, contrast sensitivity with glare (6.3°), and 25-item National Eye Institute Visual Function Questionnaire (VFQ-25) scores for General Vision were slightly but significantly better in the ZXR00V group than in the ZMB00 group (p<0.05, Wald test). At high-performance levels, the two IOL groups had different characteristics regarding various visual performance parameters.

To develop our proposed technology method to improve retinal pigment epithelium (RPE) detection in optical coherence tomography (OCT) images and compare its efficacy with Topcon’s automated segmentation algorithm across multiple retinal diseases and healthy eyes. OCT images from 88 patients with age-related macular degeneration (AMD) were used for our proposed technology model training and validation. For testing with separate images were obtained from patients with AMD (100), diabetic retinopathy (DR; 50), epiretinal membrane (ERM; 50), branch retinal vein occlusion (BRVO; 50), and healthy eyes (50). The proposed technology was used to identify RPE in OCT images using the Pyramid Scene Parsing Network on top of ResNet-50. The accuracy of the proposed technology method in RPE detection was measured using the mean absolute error (MAE) and compared with Topcon’s automated segmentation algorithm for each retinal condition. As compared with Topcon’s automated segmentation algorithm, the proposed technology showed significantly better MAEs across all conditions: AMD (2.18 vs. 4.79), DR (1.69 vs. 3.17), ERM (1.50 vs. 2.67), BRVO (1.86 vs. 2.98), and healthy eyes (1.59 vs. 2.28). Notably, the proposed technology’s superiority was most evident in the AMD group. The proposed technology method outperformed Topcon’s automated segmentation algorithm in accurately visualizing RPE in OCT images across all tested conditions, especially in AMD. Our results indicate the proposed technology’s potential to elevate the RPE segmentation which can lead to enhancing ophthalmology care by providing more accurate OCT imaging analyses.