Effective low-cost pediatric vision screening by naive nonophthalmic examiners using the 'Arclight' device.

PURPOSE: To explore whether a low-cost ophthalmoscope (Arclight) can be used by naive nonophthalmic examiners to effectively screen for pediatric eye disease. METHODS: Fifty-four children (108 eyes) were examined by five medical students using an Arclight. Gold standard examination was performed by an ophthalmologist using a slit lamp and indirect ophthalmoscope. Examinations performed included ophthalmoscopy of the optic disc, estimation of the cup-to-disc ratio (CDR), corneal light reflex test (CRT), Bruckner's reflex test (BRT), and evaluation of refractive error. We determined the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the nonophthalmologist's Arclight exam compared to the gold standard findings of comprehensive evaluation by pediatric ophthalmologists. RESULTS: Using the Arclight, the optic nerve exam was successfully completed in 65% of patients. CDRs above and below 0.5 could be determined with 66.7% sensitivity and 84.4% specificity. Arclight CRT measurements were significant (P < .00001) predictors of strabismus, with 80% sensitivity, 95.1%, specificity, 80% PPV, and 95.1% NPV. BRT was not a significant predictor of amblyopia, with a 34.6% sensitivity, 85.7% specificity, 69.2% PPV, and 58.5% NPV. Refractive error was estimated with a success rate of 81% for emmetropia, 38% for myopia, and 21% for hyperopia. The Arclight ease-of-use was rated on average as 4.4 (SD = 0.9) on a scale of 1 to 5, with 1 being the hardest and 5 being the easiest. CONCLUSIONS: Our study shows the Arclight as an affordable and effective alternative to the traditional ophthalmoscope for assessing eye disease in children. This device can improve eye health services in under-resourced regions.

Pigmented pyogenic granuloma of the cornea and conjunctiva: a rare bilateral presentation.

Pyogenic granuloma, also known as lobular capillary hemangioma, is a benign vascular lesion that primarily affects the skin and mucous membranes. It is not pyogenic; nor is it granulomatous. It typically arises in response to local trauma or surgery, irritation, hormonal changes, or chronic inflammation, and it sometimes occurs spontaneously. The occurrence of pigmented pyogenic granuloma in the conjunctiva and cornea without any history of trauma or surgery is extremely rare, particularly in children. We report the clinical presentation, diagnostic evaluation, and successful management of bilateral biopsy-proven conjunctival and corneal pigmented isolated pyogenic granuloma in an 11-year-old girl. No signs of recurrence were seen at the 3-months follow-up.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.

Corrigendum: Automated screening devices for vision screening in preschool children: a comparison of the PlusoptiX S12C photoscreener and retinomax K+3 autorefractor.

[This corrects the article DOI: 10.3389/fopht.2022.1049622.].

The UCI EyeMobile Preschool Vision Screening Program: Refractive Error and Amblyopia Results from the 2019-2020 School Year.

Purpose: To introduce the University of California Irvine (UCI) EyeMobile for Children preschool vision screening program and describe the ophthalmic examination results of children who failed screening with the PlusoptiX S12C photoscreener during one school year. Patients and Methods: Children aged 30-72 months were screened with the PlusoptiX using ROC mode 3 during the 2019-2020 school year. Children who failed screening were referred for comprehensive eye examination on the EyeMobile mobile clinic. Presence of amblyopia risk factors (ARFs), amblyopia, and refractive error was determined via retrospective review of records. Amblyopia was defined as unilateral if there was ≥ 2-line interocular difference in the best-corrected visual acuity (BCVA) and as bilateral if BCVA was < 20/50 for children < 4 years old and < 20/40 for children ≥ 4 years old. ARFs were defined using 2021 American Association for Pediatric Ophthalmology and Strabismus (AAPOS) instrument-based screening guidelines. Results: 5226 children were screened during the study period. Of the 546 children who failed screening, 350 (64%) obtained consent and were examined. Mean age of examined children was 4.45 years. Amblyopia was found in 8% of examined children, with unilateral amblyopia seen in 79% of amblyopic subjects. Glasses were prescribed to 246 (70.3%) children. Of the 240 children who received cycloplegic examinations, 43% had hyperopia and 30% had myopia. The positive predictive value (PPV) of the PlusoptiX screening for ARFs in children who received cycloplegic examinations was 70.4%. Conclusion: A significant proportion of Orange County preschoolers with refractive errors and amblyopia have unmet refractive correction needs. The PlusoptiX S12C photoscreener is an adequate screening device for the UCI EyeMobile for Children program, although modification of device referral criteria may lead to increased PPV. Further research is necessary to understand and overcome the barriers to childhood vision care in our community.

Automated screening devices for vision screening in preschool children: A comparison of the PlusoptiX S12C photoscreener and retinomax K+3 autorefractor.

Introduction: Automated vision screening devices such as photoscreeners and autorefractors have been used to accurately identify amblyopia, refractive amblyopia risk factors (ARFs), and refractive error in young children; however, there is conflicting data about the effectiveness of different screening devices. We compared the performance of two commercially available screening devices in preschool children. Methods: Children aged 3 to 5 years attending 5 preschools in Anaheim Elementary School District were screened with the PlusoptiX S12C photoscreener using ROC 3 referral criteria and Retinomax K+3 autorefractor in March 2022. Screened children were offered free cycloplegic eye examinations performed by optometrists on the UCI EyeMobile for Children mobile clinic. Children were evaluated for the presence of refractive ARFs using 2021 American Association for Pediatric Ophthalmology and Strabismus age-based referral criteria guidelines for instrument-based screening. Results: A total of 158 children were screened and 79 children received cycloplegic examinations. At least one refractive ARF was found in 20% of examined children, corresponding to a sensitivity/specificity/positive predictive value (PPV)/negative predictive value (NPV) of 94%/89%/68%/98% for the PlusoptiX and 100%/65%/42%/100% for the Retinomax. Discussion: In detecting refractive ARFs, the PlusoptiX was found to have a higher specificity and PPV while the Retinomax had a higher sensitivity and NPV. While both devices demonstrated a high sensitivity and NPV, we found that the PlusoptiX performed better overall as a screening device for our program as the Retinomax referred too many children.

Body mass index and percentage of body fat as indicators for obesity in an adolescent athletic population.

BACKGROUND: Body mass index (BMI) is widely accepted in determining obesity. Skinfold thickness measurements have been commonly used to determine percentage of body fat. HYPOTHESIS: The authors hypothesize that because BMI does not measure fat directly but relies on body weight alone, a large percentage of athletic adolescents will be misclassified as obese by BMI. DESIGN: Cross-sectional study. METHODS: To compare BMI and skinfold measurements as indicators for obesity in the adolescent athletic population, anthropometric data (height, weight, percentage body fat, age, and sex) were recorded from 33 896 student athletes (average age, 15 years; range, 11-19 years) during preparticipation physical examinations from 1985 to 2003. BMI was calculated from height and weight. Percentage of body fat was determined by measuring skinfold thickness. RESULTS: According to their BMI percentile, 13.31% of adolescent athletes were obese. Using the skinfold method, only 5.95% were obese. Of those classified as obese by the BMI, 62% were considered false positives by the skinfold method. In contrast, there was a 99% probability that the nonobese by BMI would not be obese by the skinfold method (negative predictive value = 0.99). CONCLUSIONS: BMI is a measurement of relative body weight, not body composition. Because lean mass weighs far more than fat, many adolescent athletes are incorrectly classified as obese based on BMI. Skinfold testing provides a more accurate body assessment than BMI in adolescent athletes. CLINICAL RELEVANCE: Correct body composition data can help to provide better diet and activity guidelines and prevent the psychological problems associated with being labeled as obese.

What Conditions Limit Sports Participation?

In brief Over 5 years, 10,540 annual preseason examinations were performed on student-athletes ranging in age from 11 to 19 years. The examining physicians concluded that 9,423 students (89.4%) passed, 1,070 (10.2%) passed with conditions, and 47 (0.4%) failed. The most common reasons cited for those who passed with conditions and those who failed were hypertension, ophthalmologic and genitourinary abnormalities, and musculoskeletal problems. The authors recommend that preparticipation exams follow guidelines based on scientific data and experience rather than tradition or anecdote.