Determining the Degree of Perceptible Static Eyelid Asymmetry and Effect of Face Inversion: A Cross-sectional Pilot Study.

PURPOSE: To determine the degree of static eyelid asymmetry required to be perceptible and whether this is affected by image inversion. METHODS: Images of 3 volunteers were digitally manipulated to have eyelid asymmetry of 0.5 mm, 1 mm, or 1.5 mm of 3 different types, upper lid ptosis, upper lid retraction, and lower lid retraction. Forty-nine laypersons stated whether the images were symmetrical or asymmetrical. A separate inversion survey, completed by 29 clinicians, consisted of symmetrical images and 1 mm asymmetrical images, with half being inverted. RESULTS: Upper lid ptosis, upper lid retraction, and lower lid retraction were not detected by most laypeople at 0.5 mm of severity (18.9%, 6.7%, 18.9% detection, respectively) but all 3 were detected by the majority of participants once asymmetry reached 1 mm severity (65.7%, 61.8%, 51.0% detection, respectively) and rose to over 70% identification at 1.5 mm (92.2%, 73.5%, 73.5% detection, respectively). Inversion of the images led to 19.7% lower rates of correct identification of asymmetrical faces compared with images presented in the correct orientation (80.7% asymmetry identified in normal images, 61.0% inverted, p < 0.001). CONCLUSIONS: All asymmetries were detectable by a majority of laypersons at a severity of 1 mm. Image inversion decreases a clinician's ability to detect a 1 mm asymmetry, indicating an impaired asymmetry perception in the intraoperative view. This study provides research to counsel patients with varying degrees of eyelid asymmetry.

All-cause mortality and cardiovascular outcomes with sodium-glucose Co-transporter 2 inhibitors, glucagon-like peptide-1 receptor agonists and with combination therapy in people with type 2 diabetes.

AIM: To assess the relationship of sodium-glucose cotransporter-2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor analogues (GLP-1RA) and their combination (SGLT2i + GLP-1RA) with 5-year risk of all-cause mortality, hospitalization and cardiovascular/macrovascular disease in people with type 2 diabetes. MATERIALS AND METHODS: Retrospective cohort analysis of 2.2 million people with type 2 diabetes receiving insulin across 85 health care organizations using a global federated health research network. Three intervention cohorts (SGLT2i, GLP-1RA and SGLT2i + GLP-1RA) were compared against a control cohort (no SGLT2i/GLP-1RA). Propensity score matching for age, ischaemic heart disease, sex, hypertension, chronic kidney disease, heart failure and glycated haemoglobin was used to balance cohorts 1:1 (SGLT2i, n = 143 600; GLP-1RA, n = 186 841; SGLT-2i + GLP-1RA, n = 108 504). A sub-analysis comparing combination and monotherapy cohorts was also performed. RESULTS: The intervention cohorts showed a reduced hazard ratio (HR, 95% confidence interval) over 5 years compared with the control cohort for all-cause mortality (SGLT2i 0.49, 0.48-0.50; GLP-1RA 0.47, 0.46-0.48; combination 0.25, 0.24-0.26), hospitalization (0.73, 0.72-0.74; 0.69, 0.68-0.69; 0.60, 0.59-0.61) and acute myocardial infarct (0.75, 0.72-0.78; 0.70, 0.68-0.73; 0.63, 0.60-0.66), respectively. All other outcomes showed a significant risk reduction in favour of the intervention cohorts. The sub-analysis showed a significant risk reduction in all-cause mortality for combination therapy versus SGLT2i (0.53, 0.50-0.55) and GLP-1RA (0.56, 0.54-0.59). CONCLUSIONS: SGLT2i, GLP-1RAs or combination therapy confers mortality and cardiovascular protection in people with type 2 diabetes over 5 years. Combination therapy was associated with the greatest risk reduction in all-cause mortality versus a propensity matched control cohort. In addition, combination therapy offers a reduction in 5-year all-cause mortality when compared directly against either monotherapy.

Painful Diabetic Peripheral Neuropathy: Practical Guidance and Challenges for Clinical Management.

Painful diabetic peripheral neuropathy (PDPN) is present in nearly a quarter of people with diabetes. It is estimated to affect over 100 million people worldwide. PDPN is associated with impaired daily functioning, depression, sleep disturbance, financial instability, and a decreased quality of life. Despite its high prevalence and significant health burden, it remains an underdiagnosed and undertreated condition. PDPN is a complex pain phenomenon with the experience of pain associated with and exacerbated by poor sleep and low mood. A holistic approach to patient-centred care alongside the pharmacological therapy is required to maximise benefit. A key treatment challenge is managing patient expectation, as a good outcome from treatment is defined as a reduction in pain of 30-50%, with a complete pain-free outcome being rare. The future for the treatment of PDPN holds promise, despite a 20-year void in the licensing of new analgesic agents for neuropathic pain. There are over 50 new molecular entities reaching clinical development and several demonstrating benefit in early-stage clinical trials. We review the current approaches to its diagnosis, the tools, and questionnaires available to clinicians, international guidance on PDPN management, and existing pharmacological and non-pharmacological treatment options. We synthesise evidence and the guidance from the American Association of Clinical Endocrinology, American Academy of Neurology, American Diabetes Association, Diabetes Canada, German Diabetes Association, and the International Diabetes Federation into a practical guide to the treatment of PDPN and highlight the need for future research into mechanistic-based treatments in order to prioritise the development of personalised medicine.

Artificial Intelligence Based Analysis of Corneal Confocal Microscopy Images for Diagnosing Peripheral Neuropathy: A Binary Classification Model.

Diabetic peripheral neuropathy (DPN) is the leading cause of neuropathy worldwide resulting in excess morbidity and mortality. We aimed to develop an artificial intelligence deep learning algorithm to classify the presence or absence of peripheral neuropathy (PN) in participants with diabetes or pre-diabetes using corneal confocal microscopy (CCM) images of the sub-basal nerve plexus. A modified ResNet-50 model was trained to perform the binary classification of PN (PN+) versus no PN (PN-) based on the Toronto consensus criteria. A dataset of 279 participants (149 PN-, 130 PN+) was used to train (n = 200), validate (n = 18), and test (n = 61) the algorithm, utilizing one image per participant. The dataset consisted of participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141), and pre-diabetes (n = 50). The algorithm was evaluated using diagnostic performance metrics and attribution-based methods (gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM). In detecting PN+, the AI-based DLA achieved a sensitivity of 0.91 (95%CI: 0.79-1.0), a specificity of 0.93 (95%CI: 0.83-1.0), and an area under the curve (AUC) of 0.95 (95%CI: 0.83-0.99). Our deep learning algorithm demonstrates excellent results for the diagnosis of PN using CCM. A large-scale prospective real-world study is required to validate its diagnostic efficacy prior to implementation in screening and diagnostic programmes.

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship.

Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases.

Informed Consent In Facial Photograph Publishing: A Cross-sectional Pilot Study To Determine The Effectiveness Of Deidentification Methods.

This study determined the effectiveness of three deidentification methods: use of a) a black box to obscure facial landmarks, b) a letterbox view to display restricted facial landmarks and c) a half letterbox view. Facial images of well-known celebrities were used to create a series of decreasingly deidentified images and displayed to participants in a structured interview session. 55.5% were recognised when all facial features were covered using a black box, leaving only the hair and neck exposed. The letterbox view proved more effective, reaching over 50% recognition only once the periorbital region, eyebrows, and forehead were visible. The half letterbox was the most effective, requiring the nose to be revealed before recognition reached over 50%, and should be the option of choice where appropriate. These findings provide valuable information for informed consent discussions, and we recommend consent to publish forms should stipulate the deidentification method that will be used.

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes.

AIMS/HYPOTHESIS: We aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of). METHODS: The AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm's generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN-) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN-, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm. RESULTS: The results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F1-score of 0.91 (95% CI 0.79, 1.0); PN-: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F1-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F1-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN- and an absence of corneal nerves for PN+ images. CONCLUSIONS/INTERPRETATION: We demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy.

Increased Early Sensitivity to Eyes in Mouthless Faces: In Support of the LIFTED Model of Early Face Processing.

The N170 ERP component is a central neural marker of early face perception usually thought to reflect holistic processing. However, it is also highly sensitive to eyes presented in isolation and to fixation on the eyes within a full face. The lateral inhibition face template and eye detector (LIFTED) model (Nemrodov et al. in NeuroImage 97:81-94, 2014) integrates these views by proposing a neural inhibition mechanism that perceptually glues features into a whole, in parallel to the activity of an eye detector that accounts for the eye sensitivity. The LIFTED model was derived from a large number of results obtained with intact and eyeless faces presented upright and inverted. The present study provided a control condition to the original design by replacing eyeless with mouthless faces, hereby enabling testing of specific predictions derived from the model. Using the same gaze-contingent approach, we replicated the N170 eye sensitivity regardless of face orientation. Furthermore, when eyes were fixated in upright faces, the N170 was larger for mouthless compared to intact faces, while inverted mouthless faces elicited smaller amplitude than intact inverted faces when fixation was on the mouth and nose. The results are largely in line with the LIFTED model, in particular with the idea of an inhibition mechanism involved in holistic processing of upright faces and the lack of such inhibition in processing inverted faces. Some modifications to the original model are also proposed based on these results.

Early sensitivity for eyes within faces: a new neuronal account of holistic and featural processing.

Eyes are central to face processing however their role in early face encoding as reflected by the N170 ERP component is unclear. Using eye tracking to enforce fixation on specific facial features, we found that the N170 was larger for fixation on the eyes compared to fixation on the forehead, nasion, nose or mouth, which all yielded similar amplitudes. This eye sensitivity was seen in both upright and inverted faces and was lost in eyeless faces, demonstrating it was due to the presence of eyes at fovea. Upright eyeless faces elicited largest N170 at nose fixation. Importantly, the N170 face inversion effect (FIE) was strongly attenuated in eyeless faces when fixation was on the eyes but was less attenuated for nose fixation and was normal when fixation was on the mouth. These results suggest the impact of eye removal on the N170 FIE is a function of the angular distance between the fixated feature and the eye location. We propose the Lateral Inhibition, Face Template and Eye Detector based (LIFTED) model which accounts for all the present N170 results including the FIE and its interaction with eye removal. Although eyes elicit the largest N170 response, reflecting the activity of an eye detector, the processing of upright faces is holistic and entails an inhibitory mechanism from neurons coding parafoveal information onto neurons coding foveal information. The LIFTED model provides a neuronal account of holistic and featural processing involved in upright and inverted faces and offers precise predictions for further testing.

Long-term working memory deficits after concussion: electrophysiological evidence.

BACKGROUND: Persistent complaints of lingering memory and concentration difficulties are common following a concussion, although the brain basis of these is unknown. Some suggest abnormalities can be found on the P300 event-related potential component, recorded using electroencephalography (EEG), despite unobservable cognitive impairments. OBJECTIVE: To examine the P300 and cognitive performance following a remote concussion during an n-back task that varies in working memory load. RESEARCH DESIGN: Seventeen participants with a remote concussion and 17 controls performed a visual n-back task in which working memory demands were systematically increased by manipulating cognitive load. Participants also completed neuropsychological and self-report measures. RESULTS: The concussion group showed a decrease in P300 amplitude compared to controls that was independent of working memory load on the n-back task. While no performance differences were observed between groups, P300 amplitude was negatively correlated with response times at higher loads in both groups. CONCLUSION: High functioning young adults with a remote concussion may have inefficient recruitment of processing resources for target identification, evident by the attenuated P300. The negative correlations between response time and P300 amplitude suggest that the time necessary to accurately respond to targets increases as the efficiency of allocating processing resources decreases during highly demanding working memory tasks.

Challenging the reliability and validity of cognitive measures: the case of the numerical distance effect.

The numerical distance effect (NDE) is one of the most robust effects in the study of numerical cognition. However, the validity and reliability of distance effects across different formats and paradigms has not been assessed. Establishing whether the distance effect is both reliable and valid has important implications for the use of this paradigm to index the processing and representation of numerical magnitude in both behavioral and neuroimaging studies. In light of this, we examine the reliability and validity of frequently employed variants (and one new variant) of the numerical comparison task: two symbolic comparison variants and two nonsymbolic comparison variants. The results of two experiments demonstrate that measures of the NDE that use nonsymbolic stimuli are far more reliable than measures of the NDE that use symbolic stimuli. With respect to correlations between measures, we find evidence that the NDE that arises using symbolic stimuli is uncorrelated with the NDE that is elicited by using nonsymbolic stimuli. Results are discussed with respect to their implications for the use of the NDE as a metric of numerical processing and representation in research with both children and adults.