An analysis of patients presenting to primary and secondary care with dermatological conditions in south-east Scotland with reference to the dermatological training of general practitioners

Cutaneous disease is thought to account for 10 -15% of patient consultations with general practitioners, but relatively little is known about the demography of dermatological conditions in primary care. The primary care study aims were to assess the proportion and diagnostic profile of dermatological conditions seen in primary care in the southeast of Scotland, and to draw comparisons with secondary dermatological care. General practitioners in 13 general practices serving a population of approximately 104,621 were asked to note all skin -related consultations during a two -week period. The case notes of these patients were reviewed, and diagnosis and treatment was recorded. Patients who had consulted for the same skin disorder on >/ =3 occasions during the previous year were invited for assessment by a consultant dermatologist. Where possible, the case notes from 10% of all consultations during the two -week study period were examined to assess accuracy of recording. The percentage of consultations relating to cutaneous disorders varied between practices, ranging from 3% to 18.8 %, with a mean of 8.4 %. Eczema accounted for 22.5 %, infections 20.3 %, and benign tumours for 11.4% of consultations with a dermatological basis. In contrast, in secondary care, benign tumours accounted for 23.8 %, malignant tumours 16.4% and eczema 16.3% of dermatological consultations. Dermatological disorders make up a significant proportion of general practitioners workload. The diagnostic profile of primary -care dermatology differs markedly from that of hospital practice. General practitioners may benefit from training specifically tailored to the common primary -care dermatological conditions.In order to plan appropriate delivery of dermatology services we need to periodically assess the type of work we undertake in secondary care and to examine changing trends in the numbers and type of referrals and the workload these referrals generate. The secondary care study aims were to quantify outpatient workload in hospital - based and private practice; to assess reasons for referral to secondary care and to examine the changes over 25 years in the diagnostic spectrum of conditions referred. During November 2005, all outpatient dermatological consultations in the south -east of Scotland were recorded. Demographic data, source of and reason for referral, diagnoses, investigations performed, treatment administered and disposal were recorded, and comparisons made with four previous studies. During the 1 -month study, attendances were recorded for 2118 new and 2796 review patients (new/ review 1:1.3, female / male 1.3:1, age range 0 -106 years). Eighty -nine per cent of new referrals came from primary care and 11% from secondary care. Fifty -seven per cent of referrals were for diagnosis and 38% for management advice. Benign tumours accounted for 33.4 %, malignant tumours 11.6 %, eczema 16% and psoriasis 7.4% of new cases. For return patients, 20% had skin cancer, 16.5% eczema, 13.4% psoriasis and 9% acne. The referral rate has risen over 25 years from 12.6 per 1000 population in 1980 to 21 per 1000 in 2005, with secondary care referrals increasing from 61 in Nov 1980 to 230 in November 2005. Attendances for benign and malignant skin tumours have increased six -fold since 1980. Patients with eczema and psoriasis account for one third of clinic visits. New referrals have risen by 67 %, with those from other specialities almost quadrupling since 1980 to 11% of the total in 2005.The following chapter examined the dermatological training received by local general practitioners. There is an absence of compulsory vocational training in dermatology for general practitioners and the core medical curriculum in some UK universities is lacking in adequate dermatology training. An anonymous postal questionnaire was circulated to 583 Lothian GPs, with a response rate of 67 %. A qualitative approach was used to detail GPs' experience of dermatology training in the locality both at undergraduate and postgraduate levels, and a quantitative approach to determine: (i) how important doctors consider postgraduate training in dermatology relative to training in other specialities, some of which are compulsory during their vocational training; (ii) what factors prevent doctors from pursuing post- graduate training in dermatology; (iii) how do GPs perceptions of the importance of dermatology training relate to their basic characteristics (type of GP, length of experience as a GP and gender): and (iv) how do GPs experience of their own competence in managing dermatology conditions relate to the length and type of training they have received. From all of these questions, an attempt was made to make some recommendations regarding the future of dermatology training for general practitioners.In total, 71% concluded that dermatology was not only an essential part of the medical core curriculum but should also be taught at postgraduate level. Most GPs concluded that dermatology training at postgraduate level was very important (40.3 %) or important (56.6 %), and 79.5% suggested that clinical training during ST years followed by regular (e.g. 5- yearly) updates would be optimum. GPs rate dermatology on a par with other specialities that are compulsory attachments for their vocational training. No statistical reason for failure to pursue postgraduate training was isolated. GPs' perception of the importance of dermatology was not significantly predicted by their individual characteristics. Receiving postgraduate training in dermatology was positively associated with doctors' perceptions of their own competence at managing skin conditions. Men felt more competent than women.Dermatology should remain an essential part of the undergraduate medical curriculum it should be encouraged as a useful clinical attachment during GP vocational training. Good clinical teaching ran perhaps jointly by a dermatologist and general practitioner should be our aspiration

The Relationship Between PTSD-Related Symptoms and Skin Disease Symptom Severity in a Dermatological Sample

Previous research has demonstrated associations between stress and physical health conditions, including skin disease. The stress cycle, including its associated hormonal responses and stress behaviors, contributes to more severe skin disease symptoms. Psychological stress and anxiety-disorder symptoms are known to be associated with skin disease severity; however, research has yet to examine the relationships between stress disorders and skin disease severity. Therefore, stress disorders (e.g., posttraumatic stress disorder [PTSD]) are hypothesized to have a similar relationship with skin disease severity, wherein greater severity of PTSD-related symptoms would be positively associated with skin disease severity. Participants from Amazon’s Mechanical Turk (N = 450) were screened for the presence of a dermatological condition in the past year. Participants (n = 311) who endorsed skin disease symptoms completed online self-report measures of skin disease characteristics and symptom severity, exposure to potentially traumatic events, PTSD-related symptoms, perceived stress, and demographic information. The sample was 68.2% white and 59.2% female, with a mean age of 33.9 years (SD = 11.0). Overall, participants reported moderate perceived stress (M = 18.9, SD = 6.6), and subthreshold symptoms of PTSD (M = 30.7, SD = 22). Bivariate correlations indicated significant positive associations between perceived stress and PTSD symptoms (r = .644, p \u3c .01), and PTSD symptom clusters (r = .560-.677, p \u3c .01). Skin disease severity was positively associated with perceived stress (r = .257, p \u3c .01), PTSD symptoms (r = .435, p \u3c .01), and all PTSD symptom clusters, with alterations in arousal and reactivity demonstrating the strongest relationship to skin disease severity (r = .428, p \u3c .01). Consistent with prior work among dermatological samples, findings indicate skin disease severity was positively, significantly correlated with perceived stress, PTSD symptoms, and PTSD symptom clusters. These results contribute to the existing literature on stress and posttraumatic stress in dermatological populations. Future research directions may include additional dimensions of stress and stressful life events, as well as clinician-rated evaluation of PTSD symptoms and skin conditions. Additionally, longitudinal research examining the temporal relationship between PTSD symptoms, stress, and skin disease is warranted

Visual In-Context Learning for Few-Shot Eczema Segmentation

Automated diagnosis of eczema from digital camera images is crucial for developing applications that allow patients to self-monitor their recovery. An important component of this is the segmentation of eczema region from such images. Current methods for eczema segmentation rely on deep neural networks such as convolutional (CNN)-based U-Net or transformer-based Swin U-Net. While effective, these methods require high volume of annotated data, which can be difficult to obtain. Here, we investigate the capabilities of visual in-context learning that can perform few-shot eczema segmentation with just a handful of examples and without any need for retraining models. Specifically, we propose a strategy for applying in-context learning for eczema segmentation with a generalist vision model called SegGPT. When benchmarked on a dataset of annotated eczema images, we show that SegGPT with just 2 representative example images from the training dataset performs better (mIoU: 36.69) than a CNN U-Net trained on 428 images (mIoU: 32.60). We also discover that using more number of examples for SegGPT may in fact be harmful to its performance. Our result highlights the importance of visual in-context learning in developing faster and better solutions to skin imaging tasks. Our result also paves the way for developing inclusive solutions that can cater to minorities in the demographics who are typically heavily under-represented in the training data

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.Comment: Early accepted to MICCAI 202

Skin disease analysis with limited data in particular Rosacea: a review and recommended framework

Recently, the rapid advancements in Deep Learning and Computer Vision technologies have introduced a new and exciting era in the field of skin disease analysis. However, there are certain challenges in the roadmap towards developing such technologies for real-life applications that must be investigated. This study considers one of the key challenges in data acquisition and computation, viz. data scarcity. Data scarcity is a central problem in acquiring medical images and applying machine learning techniques to train Convolutional Neural Networks for disease diagnosis. The main objective of this study is to explore the possible methods to deal with the data scarcity problem and to improve diagnosis with small datasets. The challenges in data acquisition for a few lamentably neglected skin conditions such as rosacea are an excellent instance to explore the possibilities of improving computer-aided skin disease diagnosis. With data scarcity in mind, the possible techniques explored and discussed include Generative Adversarial Networks, Meta-Learning, Few-Shot classification, and 3D face modelling. Furthermore, the existing studies are discussed based on skin conditions considered, data volume and implementation choices. Some future research directions are recommended