Expert Grading Research Articles

American Foregut Classification is Superior to Hill Classification in Evaluation of the Gastroesophageal Junction

Background: The American Foregut Society (AFS) classification is intended to be more comprehensive and accurate than the Hill grade in assessing the gastroesophageal junction. However, real-world data regarding predictive ability and interobserver variability are limited. Methods: We performed a multi-reader validation study on images collected from patients who underwent endoscopic evaluation of reflux with hernia provocation using both AFS and Hill classification. The primary outcome was inter-reader agreement, assessed using Fleiss K. Secondary outcomes were percentage of patients with pathologic reflux at each Hill grade or AFS grade, correlation between acid exposure time (AET) and individual components of the AFS grade, and difference in predictive ability based on expert versus non-expert graders. Results: Sixty-four eligible patients were identified. Inter-rater reliability was substantial for AFS classification ( K = 0.65) and fair for Hill classification ( K= 0.28). The AFS grade demonstrated moderate overall correlation with AET (ρ = .36) and the Hill grade demonstrated overall weak correlation (ρ = .28). No single component of the AFS score performed significantly better in predicting pathologic reflux than the AFS grade (AFS F = 9.93, P = .0025) and there was no significant difference in overall grading between expert- and non-expert readers (ρ = .38 vs ρ = .29). Conclusion: The AFS score can classify the GEJ with moderate interobserver agreement and is superior to the Hill grade in predicting acid exposure time. AFS grade has better predictive power than the individual components used in scoring and is robust to differences in reader experience.

Laser and Skin-Care Synergy: A Post-Laser Application of Novel Adaptogenic Moisturizing Serum for Improving Healing and Cosmesis on the Face.

Laser procedures are often used for global improvement in skin quality. The state of the skin is under stress after laser treatment, and it is beneficial to utilize topical agents to assist with optimal healing and cosmetic outcomes. Currently, such post-laser-cosmeceutical synergies are being investigated. Skin adaptogens are bioactive ingredients that enhance the skin's resistance and adaptation to stress. Investigate the use of an adaptogenic moisturizing serum (AMS) in synergy with post-laser treatment recovery. Eight patients underwent long-pulsed 755-nm alexandrite or 1064-nm picosecond laser treatments on the face, targeting photoaging and pigmentation. After the laser treatment, during their healing process, they utilized a novel AMS twice daily to assist with the post-laser recovery. A blinded expert grader evaluated before and after facial images of each patient using a modified Griffiths photodamage photonumeric scale. The findings revealed that the AMS was well-tolerated with no reported adverse events, and elicited aesthetically pleasing results. There was a significant decrease in photodamage scores following the treatment (p = 0.0078) with a median reduction of 3.5. Patients had improvement in skin photodamage, pigmentation, and skin radiance in clinic and photography after a combination of laser and AMS serum treatments. Utilizing the adaptogen moisturizing serum skin care regimen after the laser treatments likely assisted in the skin's healing process. As a result, we propose that there is a potential benefit of combining certain laser treatments and an adaptogen-based skin care regimen for better aesthetic outcomes and skin recuperation post-procedure.

Automated Detection of Drusenoid Pigment Epithelial Detachments From Spectral-Domain Optical Coherence Tomography in Patients With AMD.

This study aimed to develop an algorithm for automated detection of drusenoid pigment epithelial detachments (DPEDs) in optical coherence tomography (OCT) volumes of patients with age-related macular degeneration (AMD) and to compare its performance against traditional reading center grading on color-fundus photographs (CFPs). Eyes with a range of AMD severities, excluding neovascular disease, were imaged using spectral-domain OCT (SD-OCT) and paired CFPs and were followed annually for up to 5years. DPEDs were automatically identified by segmenting the retinal pigment epithelium (RPE) and Bruch's membrane (BM) layers from the SD-OCT volumes and imposing both a minimum RPE BM height (>75 µm) and a two-dimensional length requirement (>433 µm). Comparisons in detection rates and contoured areas were made between the algorithmic SD-OCT detections and manually graded and contoured CFPs. Of the 1602 visits for the 323 eyes, the automated OCT algorithm identified 139 visits (8.7%) from 50 eyes with DPED, but a reading center review of paired CFPs identified 23 visits (1.4%) from nine eyes as having DPEDs. Eyes identified with DPEDs on OCT received nine-step AMD severity scores ranging from 6 to 10, and those scores had occurrence ratios of 23/160 (14%), 89/226 (39%), 24/99 (24%), 2/63 (3%), and 1/29 (3%), respectively. On a subset of 25 visits that also underwent manual contouring of DPED lesions in CFP, the Pearson correlation coefficient for DPED areas observed by OCT and CFP was 0.85. Our analysis shows the feasibility of using OCT scans to objectively detect features that historically have been detected qualitatively by expert graders on CFPs. Automated detection and quantitation of high-risk features can facilitate screening patients for clinical-trial enrollment and could serve as an outcome metric [T1 (Translation-to-Humans) and T4 (Translation-to-Population-Health)].

Efficacy of Topical Hydroxypinacolone Retinoate-Peptide Product Versus Fractional CO2 Laser in Facial Aging.

Many people are interested in addressing visible signs of aging with non-invasive cosmetic treatments. Development of effective topical products will provide options to delay or support cosmetic procedures. This study assessed and compared the efficacy and tolerance of a topical product used over the course of 16 weeks to a single ablative laser treatment on women with moderate global photodamage on the face. Subjects in Cell 1 (Laser Cell) were treated over the entire face with a fractional CO2 laser system. Subjects in Cell 2 (Topical Serum Cell) were treated with a topical serum containing hydroxypinacolone retinoate and peptides over the entire face, twice per day for 16 weeks. The study was composed of 71 women, with 29 in the Laser Cell (mean age 56.2) and 42 in the Topical Serum Cell (mean age 55.0), between 40 and 65 years old. Expert grading was used to determine efficacy parameters. Participants in the Topical Serum Cell achieved more significant improvement (p < 0.05) in Marionette lines, fine lines (global face), wrinkles (global face), wrinkles (crow's feet), nasolabial folds, texture, smoothness (tactile), global hyperpigmentation, lift, and photodamage compared to participants in the Laser Cell. Participants in the Topical Serum Cell achieved parity in the look of fine lines (crow's feet), forehead lines, glabella, firmness/bounce (tactile), skin tone evenness, radiance. While no statistically significant differences in tolerability were observed, treatment with the topical cosmetic product achieved parity or statistically better improvement in parameters compared to laser treatment at 16 weeks.

Comparison of remote and in-person respiratory function grading of brachycephalic dogs.

To compare the reliability of respiratory function grading (RFG) scores assigned in-person and remotely via video and electronic stethoscope recordings, evaluated by novice and expert graders. Prospective study. Fifty-seven brachycephalic dogs. Dogs were evaluated in person by expert graders and RFG scores were assigned. Audio and video recordings were made during the in-person evaluations. Four expert and four novice graders evaluated the recordings and assigned an RFG score to each dog. Agreement between in-person and remote RFG scores was assessed using Cohen's kappa statistic. Interobserver reliability was assessed using Fleiss' kappa statistic. The median RFG score from the in-person assessment was 1 (range, 0-3). Distribution of RFG scores included 12 grade 0 scores, 19 grade 1 scores, 25 grade 2 scores, and 1 grade 3 score. The raw percentage agreements between remote and in-person scores were 68.4%, 59.6%, 64.9%, and 61.4% for the four experts, and 52.6%, 64.9%, 50.9%, and 42.1% for the four novices. Reliability between remote and in-person RFG scores was poor to moderate both for the experts (Cohen's kappa: .48, .37, .46, .41) and novices (Cohen's kappa: .28, .47, .28, .21). Interobserver reliability was moderate among the experts (Fleiss' kappa: .59) and poor among the novices (Fleiss' kappa: .39). Remote RFG scores had poor to moderate interassessment and interobserver reliability. Novice evaluators performed worse than experts for remote or in-person RFG evaluations. Remote RFG, as measured in this study, is not reliable for assigning RFG scores. Modifications could be made to remote evaluation to improve reliability. Based upon the performance of novice evaluators, training of evaluators is justified.

Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential for human error. This study describes the development and evaluation of machine learning models intended to reduce cost and improve reliability of these surveys. Fifty-six thousand seven hundred twenty-five everted eyelid photographs were obtained from 11,358 children of age 0 to 9 years in a single trachoma-endemic region of Ethiopia over a 3-year period. Expert graders reviewed all images from each examination to determine the estimated number of tarsal conjunctival follicles and the degree of trachomatous inflammation-intense. The median estimate of the 3 grader groups was used as the ground truth to train a MobileNetV3 large deep convolutional neural network to detect cases with TF. The classification model predicted a TF prevalence of 32%, which was not significantly different from the human consensus estimate (30%; 95% confidence interval of difference, -2 to +4%). The model had an area under the receiver operating characteristic curve of 0.943, F1 score of 0.923, 88% accuracy, 83% sensitivity, and 91% specificity. The area under the receiver operating characteristic curve increased to 0.995 when interpreting nonborderline cases of TF. Deep convolutional neural network models performed well at classifying TF and detecting the number of follicles evident in conjunctival photographs. Implementation of similar models may enable accurate, efficient, large-scale trachoma screening. Further validation in diverse populations with varying TF prevalence is needed before implementation at scale.

Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

Severity of Trachomatous Scarring among Adults in Trachoma-Endemic Amhara Region of Ethiopia.

Trachomatous scarring has been shown to progress regardless of active ocular Chlamydia trachomatis infection, indicating that scarring drivers may be unrelated to ongoing transmission. Although scarring prevalence is commonly associated with older age and female sex, less is known about other potential contributors to its development. This study identified and assessed risk factors associated with scarring magnitude in a trachoma-endemic setting, utilizing a five-point photographic scale (S0-S4). During 2017 trachoma surveys of Amhara, Ethiopia, photographers captured left and right conjunctival images of adults (ages 15 years and older) from 10 districts. Subsequently, two graders independently assessed photographs for scarring, with discrepancies adjudicated by an expert grader. Scarring scores for 729 individuals were aggregated from the eye level to the participant level, excluding 17 participants because of poor photograph quality. Among those with scarring, most cases (20.4%) were severe (S4, comprising more than 90% of the tarsal conjunctiva) compared with the prevalence of moderate S3-A/B (11.2%), S2 (8.3%), and mild S1 (19.2%). The youngest group (ages 15-19 years) exhibited all scarring stages. Older participants (60 years and older) experienced a greater burden of severe scarring (S4 prevalence: 32.6%) than their younger (15-19 years) counterparts (6.2%). Multivariate ordinal logistic regression models indicated female sex, increasing age, and district-level trachomatous follicular-inflammation prevalence were significant predictors of scarring severity. Trachomatous scarring and its progression to trichiasis, may prove a barrier to meeting WHO timelines for trachoma elimination and will necessitate ongoing surveillance and interventions after elimination thresholds have been met.

Mitigation of ultraviolet-induced erythema and inflammation by para-hydroxycinnamic acid in human skin.

To evaluate whether p-hydroxycinnamic acid (pHCA) alone and in combination with niacinamide (Nam) can mitigate UV-induced erythema, barrier disruption, and inflammation. Three independent placebo-controlled double-blinded studies were conducted on female panellists who were pretreated on sites on their backs for 2 weeks with skin care formulations which contained 0.3% or 1% pHCA with 5% Nam, 1% pHCA alone, 1.8% octinoxate, or control formula. Treated sites were then exposed to 1.5 minimal erythemal dose (MED) solar simulated radiation (SSR) and had chromameter and expert grading measures for erythema, barrier integrity via TEWL, and the skin surface IL-1RA/IL-1α inflammatory biomarkers isolated from D-Squame tapes. Across the three independent studies, pHCA alone or in combination with Nam showed a significant mitigation of UV-induced erythema, barrier disruption, and levels of the surface inflammatory biomarkers IL-1RA/IL-1α. The cinnamate analogue Octinoxate did not replicate the effects of pHCA. The study results show that pHCA alone or in combination with Nam can mitigate UV-induced damage to skin. These include mitigation of UV-induced erythema as measured by instrument and expert grade visualization. Additionally, pHCA with Nam protected damage to the barrier and reduced the induction of the SASP-related surface inflammatory biomarker IL-1RA/IL-1α. The inability of Octinoxate to have any protective effect and the detection of low levels of pHCA on skin surface after 24 h of application supports that these effects are based on a biological response to pHCA. These findings add to the body of evidence that pHCA alone or in combination with Nam can enhance the skin's biological response to UV-induced damage. This supports pHCA can potentially impact aging and senescence, thereby maintain skin's functionality and appearance.

Evaluation of an AI algorithm trained on an ethnically diverse dataset to screen a previously unseen population for diabetic retinopathy.

This study aimed to determine the generalizability of an artificial intelligence (AI) algorithm trained on an ethnically diverse dataset to screen for referable diabetic retinopathy (RDR) in the Armenian population unseen during AI development. This study comprised 550 patients with diabetes mellitus visiting the polyclinics of Armenia over 10 months requiring diabetic retinopathy (DR) screening. The Medios AI-DR algorithm was developed using a robust, diverse, ethnically balanced dataset with no inherent bias and deployed offline on a smartphone-based fundus camera. The algorithm here analyzed the retinal images captured using the target device for the presence of RDR (i.e., moderate non-proliferative diabetic retinopathy (NPDR) and/or clinically significant diabetic macular edema (CSDME) or more severe disease) and sight-threatening DR (STDR, i.e., severe NPDR and/or CSDME or more severe disease). The results compared the AI output to a consensus or majority image grading of three expert graders according to the International Clinical Diabetic Retinopathy severity scale. On 478 subjects included in the analysis, the algorithm achieved a high classification sensitivity of 95.30% (95% CI: 91.9%-98.7%) and a specificity of 83.89% (95% CI: 79.9%-87.9%) for the detection of RDR. The sensitivity for STDR detection was 100%. The study proved that Medios AI-DR algorithm yields good accuracy in screening for RDR in the Armenian population. In our literature search, this is the only smartphone-based, offline AI model validated in different populations.

Yellowness in skin complexion: Analysis of self-perception of women in China evaluated against clinical parameters of yellowness.

Skin "yellowness" is an abstract and subjective term, without a definitive measurement protocol. Objectives were to analyze Chinese women's self-perception of skin yellowness and associated parameters and identify objective clinical measurements that correlate with these perceptions. Following focus group discussions, criteria for skin yellowness were defined, and validated by volunteer rankings of facial images. A typology study of 185 women was performed. Participants were grouped into yellow (Color Uniformity, Brightness and Transparency (CUBT) yellow scale grade>3, chromameter b* value>16) and non-yellow (CUBT yellow scale grade<2, b* value<14) groups. Participants self-evaluated their skin on yellowness, transparency, skin uniformity, dullness, radiance, oiliness, and texture. Expert assessments were performed to grade sebaceous pores, ocular area pigmentation, pigmentary spots and CUBT scores. Instrumental analysis of the skin was employed using corneometer, sebumeter, mexameter chromameter, and AGE reader. Women in the yellow group self-evaluated their skin as significantly duller, less uniform, and less radiant than women in the non-yellow group (P≤0.05). Higher levels of ocular area pigmentation and lower facial skin uniformity and brightness (P<0.001) were observed in women with yellow skin. CUBT expert grading showed lower pink skin color, but significantly higher beige, yellow, and olive pigmentation (P≤0.05) in women in the yellow skin group. Melanin and b* values were significantly higher in women with yellow skin while L value was significantly lower. Self-perceived skin yellowness in Chinese women correlates to chromameter and mexameter measurements, as well as expert evaluation of ocular pigmentation and CUBT parameters.

Automated magnetic resonance imaging-based grading of the lumbar intervertebral disc and facet joints.

Degeneration of both intervertebral discs (IVDs) and facet joints in the lumbar spine has been associated with low back pain, but whether and how IVD/joint degeneration contributes to pain remains an open question. Joint degeneration can be identified by pairing T1 and T2 magnetic resonance imaging (MRI) with analysis techniques such as Pfirrmann grades (IVD degeneration) and Fujiwara scores (facet degeneration). However, these grades are subjective, prompting the need to develop an automated technique to enhance inter-rater reliability. This study introduces an automated convolutional neural network (CNN) technique trained on clinical MRI images of IVD and facet joints obtained from public-access Lumbar Spine MRI Dataset. The primary goal of the automated system is to classify health of lumbar discs and facet joints according to Pfirrmann and Fujiwara grading systems and to enhance inter-rater reliability associated with these grading systems. Performance of the CNN on both the Pfirrmann and Fujiwara scales was measured by comparing the percent agreement, Pearson's correlation and Fleiss kappa value for results from the classifier to the grades assigned by an expert grader. The CNN demonstrates comparable performance to human graders for both Pfirrmann and Fujiwara grading systems, but with larger errors in Fujiwara grading. The CNN improves the reliability of the Pfirrmann system, aligning with previous findings for IVD assessment. The study highlights the potential of using deep learning in classifying the IVD and facet joint health, and due to the high variability in the Fujiwara scoring system, highlights the need for improved imaging and scoring techniques to evaluate facet joint health. All codes required to use the automatic grading routines described herein are available in the Data Repository for University of Minnesota (DRUM).

Evaluating Chatbot responses to patient questions in the field of glaucoma.

The aim of this study was to evaluate the accuracy, comprehensiveness, and safety of a publicly available large language model (LLM)-ChatGPT in the sub-domain of glaucoma. Evaluation of diagnostic test or technology. We seek to evaluate the responses of an artificial intelligence chatbot ChatGPT (version GPT-3.5, OpenAI). We curated 24 clinically relevant questions in the domain of glaucoma. The questions spanned four categories: pertaining to diagnosis, treatment, surgeries, and ocular emergencies. Each question was posed to the LLM and the responses obtained were graded by an expert grader panel of three glaucoma specialists with combined experience of more than 30 years in the field. For responses which performed poorly, the LLM was further prompted to self-correct. The subsequent responses were then re-evaluated by the expert panel. Accuracy, comprehensiveness, and safety of the responses of a public domain LLM. There were a total of 24 questions and three expert graders with a total number of responses of n = 72. The scores were ranked from 1 to 4, where 4 represents the best score with a complete and accurate response. The mean score of the expert panel was 3.29 with a standard deviation of 0.484. Out of the 24 question-response pairs, seven (29.2%) of them had a mean inter-grader score of 3 or less. The mean score of the original seven question-response pairs was 2.96 which rose to 3.58 after an opportunity to self-correct (z-score - 3.27, p = 0.001, Mann-Whitney U). The seven out of 24 question-response pairs which performed poorly were given a chance to self-correct. After self-correction, the proportion of responses obtaining a full score increased from 22/72 (30.6%) to 12/21 (57.1%), (p = 0.026, χ2 test). LLMs show great promise in the realm of glaucoma with additional capabilities of self-correction. The application of LLMs in glaucoma is still in its infancy, and still requires further research and validation.

Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.

To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA). This cross-sectional study included 202 participants with a full range of diabetic retinopathy (DR) severities (diabetes mellitus without retinopathy, mild to moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR) and 39 healthy participants. Consecutive 6 × 6-mm OCTA scans at the central macula, optic disc, and temporal region in one eye from 202 participants in a clinical DR study were acquired with a 70-kHz OCT commercial system (RTVue-XR). Widefield OCTA en face images were generated by montaging the scans from these three regions. A projection-resolved OCTA algorithm was applied to remove projection artifacts at the voxel scale. A deep convolutional neural network with a parallel U-Net module was designed to detect NPAs and distinguish signal reduction artifacts from flow deficits in the superficial vascular complex (SVC), intermediate capillary plexus (ICP), and deep capillary plexus (DCP). Expert graders manually labeled NPAs and signal reduction artifacts for the ground truth. Sixfold cross-validation was used to evaluate the proposed algorithm on the entire dataset. The proposed algorithm showed high agreement with the manually delineated ground truth for NPA detection in three retinal vascular plexuses on widefield OCTA (mean ± SD F-score: SVC, 0.84 ± 0.05; ICP, 0.87 ± 0.04; DCP, 0.83 ± 0.07). The extrafoveal avascular area in the DCP showed the best sensitivity for differentiating eyes with diabetes but no retinopathy (77%) from healthy controls and for differentiating DR by severity: DR versus no DR, 77%; referable DR (rDR) versus non-referable DR (nrDR), 79%; vision-threatening DR (vtDR) versus non-vision-threatening DR (nvtDR), 60%. The DCP also showed the best area under the receiver operating characteristic curve for distinguishing diabetes from healthy controls (96%), DR versus no DR (95%), and rDR versus nrDR (96%). The three-plexus-combined OCTA achieved the best result in differentiating vtDR and nvtDR (81.0%). A deep learning network can accurately segment NPAs in individual retinal vascular plexuses and improve DR diagnostic accuracy. Using a deep learning method to segment nonperfusion areas in widefield OCTA can potentially improve the diagnostic accuracy of diabetic retinopathy by OCT/OCTA systems.

Effect of manual OCTA segmentation correction to improve image quality and visibility of choroidal neovascularization in AMD

In this retrospective case series on neovascular age-related macular degeneration (nAMD), we aimed to improve Choroidal Neovascularization (CNV) visualization in Optical Coherence Tomography Angiography (OCTA) scans by addressing segmentation errors. Out of 198 eyes, 73 OCTA scans required manual segmentation correction. We compared uncorrected scans to those with minimal (2 corrections), moderate (10 corrections), and detailed (50 corrections) efforts targeting falsely segmented Bruch’s Membrane (BM). Results showed that 55% of corrected OCTAs exhibited improved quality after manual correction. Notably, minimal correction (2 scans) already led to significant improvements, with additional corrections (10 or 50) not further enhancing expert grading. Reduced background noise and improved CNV identification were observed, with the most substantial improvement after two corrections compared to baseline uncorrected images. In conclusion, our approach of correcting segmentation errors effectively enhances image quality in OCTA scans of nAMD. This study demonstrates the efficacy of the method, with 55% of resegmented OCTA images exhibiting enhanced quality, leading to a notable increase in the proportion of high-quality images from 63 to 83%.

Beneficial Effects of Multi-Micronutrient Supplementation with Collagen Peptides on Global Wrinkles, Skin Elasticity and Appearance in Healthy Female Subjects.

With ageing, collagen production slows down, leading to wrinkle appearance and loss of elasticity. Replenishing key structural molecules through oral supplementation is a promising strategy that complements the topical delivery of cosmetic products and creates a holistic skincare regimen. The present study assessed the effectiveness of a food supplement with collagen peptides, vitamins and minerals in improving the quality of the skin and general wellbeing of healthy women. This was an open-label study of 135 women aged between 45 and 65years. A 3-month treatment phase followed a 4-week washout phase, with visits scheduled at baseline and after each month of treatment. Subjects received three tablets of Richelet Skin Renewal daily. The primary outcome was change from baseline to month3 in global wrinkles score by expert grader analysis. Secondary outcomes included changes in skin elasticity and other skin attributes, product assessment via self-perception questionnaires and total antioxidant status. A total of 116 subjects completed the study. The mean global wrinkles score indicated a statistically significant decrease from 5.9 at baseline to 5.0 at month3 (p < 0.0001), with 83.6% of subjects showing an improvement; significant changes were reported at all intermediate visits. The increase in skin elasticity was also statistically significant (R2 score 0.74 at month3; p < 0.0001). All subjects (100%) demonstrated significant improvements in skin texture, skin tone evenness, skin radiance and overall skin quality at the month3 visit. The study product achieved statistically significant, noticeable effects on global wrinkles, skin elasticity and a range of skin attributes after 3months of use in healthy women. These results strengthen the evidence for supplementation of collagen peptides and other micronutrients as an effective component of anti-ageing skincare.

Quantifying image quality in AOSLO images of photoreceptors.

The use of "quality" to describe the usefulness of an image is ubiquitous but is often subject to domain specific constraints. Despite its continued use as an imaging modality, adaptive optics scanning light ophthalmoscopy (AOSLO) lacks a dedicated metric for quantifying the quality of an image of photoreceptors. Here, we present an approach to evaluating image quality that extracts an estimate of the signal to noise ratio. We evaluated its performance in 528 images of photoreceptors from two AOSLOs, two modalities, and healthy or diseased retinas. The algorithm was compared to expert graders' ratings of the images and previously published image quality metrics. We found no significant difference in the SNR and grades across all conditions. The SNR and the grades of the images were moderately correlated. Overall, this algorithm provides an objective measure of image quality that closely relates to expert assessments of quality in both confocal and split-detector AOSLO images of photoreceptors.

Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses

The objective of this study was to develop calibration models against rib eye traits and independently validate the precision, accuracy, and repeatability of the Frontmatec Q-FOM™ Beef grading camera in Australian carcasses. This study compiled 12 different research datasets acquired from commercial processing facilities and were comprised of a diverse range of carcass phenotypes, graded by industry identified expert Meat Standards Australia (MSA) graders and sampled for chemical intramuscular fat (IMF%). Calibration performance was maintained when the device was independently validated. For continuous traits, the Q-FOM™ demonstrated precise (root mean squared error of prediction, RMSEP) and accurate (coefficient of determination, R2) prediction of eye muscle area (EMA) (R2 = 0.89, RMSEP = 4.3 cm2, slope = 0.96, bias = 0.7), MSA marbling (R2 = 0.95, RMSEP = 47.2, slope = 0.98, bias = −12.8) and chemical IMF% (R2 = 0.94, RMSEP = 1.56%, slope = 0.96, bias = 0.64). For categorical traits, the Q-FOM™ predicted 61%, 64.3% and 60.8% of AUS-MEAT marbling, meat colour and fat colour scores equivalent, and 95% within ±1 classes of expert grader scores. The Q-FOM™ also demonstrated very high repeatability and reproducibility across all traits.

Double-blind, vehicle-controlled clinical investigation of peptide OS-01 for skin rejuvenation.

Senescent cells contribute to age-related tissue deterioration, including the skin, which plays important roles in overall health and social interactions. This study aimed to assess the effects of the senotherapeutic peptide, OS-01 (a.k.a. Pep 14), on skin. A 12-week split-face, double-blinded, vehicle-controlled study involving 22 participants was conducted. The OS-01-containing formulation was applied to one side of the face, while the other side received an identical control formulation lacking the peptide. Skin characteristics were assessed using instrumental measurements, expert clinical grading, and subjective questionnaires. Results showed that the OS-01 formulation significantly improved one aspect of skin barrier function, as evidenced by reduced trans-epidermal water loss compared to both baseline and vehicle control. Expert grading and Antera 3D image analysis revealed a reduction in wrinkle appearance and indentation in the periorbital area, and improved skin texture and radiance on both sides of the face, with the OS-01-containing formulation demonstrating superior results. Participants also perceived improvements in skin hydration, smoothness, radiance, and overall appearance. The findings suggest that the OS-01 formulation promotes skin health by strengthening the skin barrier, protecting against dehydration, reducing the appearance of wrinkles, and improving skin texture and radiance. These effects are likely attributed to the senotherapeutic properties of OS-01 in reducing cellular senescence and its associated detrimental effects.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2

Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.