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. Graphical

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.

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.

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.

This study aimed to evaluate the relative association between sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1Ra) with the incidence of gout in patients with type 2 diabetes (T2D) using real-world data. We conducted a cohort study using data from TriNetX (an international federated database). We included patients commenced on metformin or insulin, either alone or with an SGLT2i or GLP-1Ra, at least 2 years prior to date of analysis. We propensity score matched (PSM) (1:1) for 26 relevant characteristics. Time to event analysis was performed to assess the incidence of gout, all-cause mortality (positive control), and herpes zoster infection (negative control) at 5 years following drug initiation. Prior to PSM, the cohort numbers were as follows: metformin control, 1,111,449; SGLT2i with metformin, 101,706; GLP-1Ra with metformin, 110,180, insulin control, 1,398,066; SGLT2i with insulin, 68,697; and GLP-1Ra with insulin, 99,693. SGLT2i with metformin demonstrated a statistically significant decreased incidence of gout at 5 years compared to the metformin control cohort (HR 0.75 [95% CI 0.69-0.82], P < 0.0001). Similarly, SGLT2i with insulin demonstrated a statistically significant decreased incidence of gout at 5 years compared to the insulin control cohort (HR 0.83 [95% CI 0.74-0.92], P < 0.0001). Conversely, no significant disparity in gout incidence was observed between the use of GLP-1Ra and matched controls. Subgroup analysis showed an associated reduced incidence of gout with SGLT2i use compared to GLP-1Ra, in groups using metformin (HR 0.77 [95% CI 0.70-0.86], P < 0.0001) or insulin (HR 0.82 [95% CI 0.73-0.91)], P < 0.0001). In this large-scale real-world study, SGLT2i use was associated with a lower incidence of gout in patients with T2D compared to both insulin and metformin controls. These findings suggest the potential of SGLT2i as a promising therapeutic option for treating gout in this population.

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.