RESUMEN
BACKGROUND: Facial contour naturally decreases the visual field. Peripheral visual field defects caused by facial anatomy and ocular pathology can be missed in a routine standard of care. Mathematically calculating the true angle for turning the head to optimize the peripheral visual field has not been studied to date. The purpose of this study was to explore the utility of turning the head during perimetry to maximize the testable visual field. METHODS: Six healthy study participants aged 18-52 were enrolled, prospectively; the dominant eye of each participant was tested. In total, 60-4 visual fields were obtained from each participant's dominant eye with the head in primary position. Then, the 60-4 tests were repeated with the head turned prescribed degrees toward and away from the tested eye ("manual method"). Based on a photograph of the participant's face, a convolutional neural network (CNN) was used to predict the optimal head turn angle for maximizing the field, and the test was repeated in this position ("automated method"). RESULTS: Maximal visual field exposure was found at a head turn of 15° away from the tested eye using the manual method and was found at an average head turn of 12.6° using the automated method; maximum threshold values were similar between manual and automated methods. The mean of threshold in these subjects at the standard direction and the predicted optimum direction was 1,302, SD = 69.35, and 1,404, SD = 67.37, respectively (P = 0.02). CONCLUSIONS: Turning the head during perimetry maximizes the testable field area by minimizing the influence of prominent facial anatomy. In addition, our CNN can accurately predict each individual's optimal angle of head turn for maximizing the visual field.
RESUMEN
BACKGROUND: Clinical guidelines for most adults with diabetes recommend maintaining hemoglobin A1c (HbA1c) levels ≤7% (≤53 mmol/mol) to avoid microvascular and macrovascular complications. People with diabetes of different ages, sexes, and socioeconomic statuses may differ in their ease of attaining this goal. OBJECTIVE: As a team of people with diabetes, researchers, and health professionals, we aimed to explore patterns in HbA1c results among people with type 1 or type 2 diabetes in Canada. Our research question was identified by people living with diabetes. METHODS: In this patient-led retrospective cross-sectional study with multiple time points of measurement, we used generalized estimating equations to analyze the associations of age, sex, and socioeconomic status with 947,543 HbA1c results collected from 2010 to 2019 among 90,770 people living with type 1 or type 2 diabetes in Canada and housed in the Canadian National Diabetes Repository. People living with diabetes reviewed and interpreted the results. RESULTS: HbA1c results ≤7.0% represented 30.5% (male people living with type 1 diabetes), 21% (female people living with type 1 diabetes), 55% (male people living with type 2 diabetes) and 59% (female people living with type 2 diabetes) of results in each subcategory. We observed higher HbA1c values during adolescence, and for people living with type 2 diabetes, among people living in lower income areas. Among those with type 1 diabetes, female people tended to have lower HbA1c levels than male people during childbearing years but higher HbA1c levels than male people during menopausal years. Team members living with diabetes confirmed that the patterns we observed reflected their own life courses and suggested that these results be communicated to health professionals and other stakeholders to improve the treatment for people living with diabetes. CONCLUSIONS: A substantial proportion of people with diabetes in Canada may need additional support to reach or maintain the guideline-recommended glycemic control goals. Blood sugar management goals may be particularly challenging for people going through adolescence or menopause or those living with fewer financial resources. Health professionals should be aware of the challenging nature of glycemic management, and policy makers in Canada should provide more support for people with diabetes to live healthy lives.
RESUMEN
BACKGROUND: Despite, the potential clinical utility of 60-4 visual fields, they are not frequently used in clinical practice partly, due to the purported impact of facial contour on field defects. The purpose of this study was to design and test an artificial intelligence-driven platform to predict facial structure-dependent visual field defects on 60-4 visual field tests. METHODS: Subjects with no ocular pathology were included. Participants were subject to optical coherence tomography, 60-4 Swedish interactive thresholding algorithm visual field tests and photography. The predicted visual field was compared with observed 60-4 visual field results in subjects. Average and point-specific sensitivity, specificity, precision, negative predictive value, accuracy, and F1-scores were primary outcome measures. RESULTS: 30 healthy were enrolled. Three-dimensional facial reconstruction using a convolution neural network (CNN) was able to predict facial contour-dependent 60-4 visual field defects in 30 subjects without ocular pathology. Overall model accuracy was 97%±3% and 96%±3% and the F1-score, dependent on precision and sensitivity, was 58%±19% and 55%±15% for the right eye and left eye, respectively. Spatial-dependent model performance was observed with increased sensitivity and precision within the far inferior nasal field reflected by an average F1-score of 76%±20% and 70%±29% for the right eye and left eye, respectively. CONCLUSIONS: This pilot study reports the development of a CNN-enhanced platform capable of predicting 60-4 visual field defects in healthy controls based on facial contour. Further study with this platform may enhance understanding of the influence of facial contour on 60-4 visual field testing.
