Opinion statementDermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended. The increased availability of polarized dermatoscopes, and the extended use of dermatoscopy in non-pigmented lesions led to the discovery of new criteria, and we recommend that lesions should be examined with polarized and non-polarized dermatoscopy. The “chaos and clues algorithm” is a good starting point for beginners because it is easy to use, accurate, and it works for all types of pigmented lesions not only for those melanocytic. Physicians, who use dermatoscopy routinely, should be aware of new clues for acral melanomas, nail matrix melanomas, melanoma in situ, and nodular melanoma. Dermatoscopy should also be used to distinguish between different subtypes of basal cell carcinoma and to discriminate highly from poorly differentiated squamous cell carcinomas to optimize therapy and management of non-melanoma skin cancer. One of the most exciting areas of research is the use of dermatoscopic images for machine learning and automated diagnosis. Convolutional neural networks trained with dermatoscopic images are able to diagnose pigmented lesions with the same accuracy as human experts. We humans should not be afraid of this new and exciting development because it will most likely lead to a peaceful and fruitful coexistence of human experts and decision support systems.

Background Dirofilariosis is a vector-borne parasitosis caused by filarial nematodes of the genus Dirofilaria. In humans, who represent accidental hosts, dirofilariosis is mostly caused by Dirofilaria repens and Dirofilaria immitis. In Austria, the first reported case occurred in 1978. Since then, several (case) reports have been published. Methods A systematic and retrospective review of collected published cases and new, unpublished confirmed cases of human dirofilariosis occurring in Austria was performed. A nematode was extracted from the eyelid of a previously unreported case and subsequently characterized histologically and using molecular biology techniques. Results Data on a total of 39 cases of human dirofilariosis in Austria occurring between 1978 and 2020 are summarized. Over the past four decades the incidence has markedly increased, in particular after 1998. Of the 39 patients, men and women were equally affected, and the mean age was 47.1 years. The area most frequently affected was the head (38.5% of cases). Confined ocular involvement was observed in 23.1% of cases, and nematodes were isolated from the neck/trunk, extremities and the genito-inguinal area in 25.6, 15.4 and 15.4% of patients, respectively. Microfilariae were detected in two cases. Of the 39 patients, only 73.9% tested positive for anti-filarial antibodies and 56.3% for eosinophilia, despite successful isolation of a nematode; consequently, these measures did not represent reliable markers for dirofilariosis. Most patients had a travel history to countries endemic for Dirofilaria species. One patient who had not traveled abroad represented the only autochthonous case recorded to date. Dirofilaria repens was the predominant species, identified in 89.7% of cases. In the newly reported case of subcutaneous dirofilariosis, a live non-gravid Dirofilaria repens adult female of 12 cm length was isolated from the eyelid of the patient, and a video of the extraction is provided. Conclusions The incidence of human dirofilariosis cases has increased strikingly over the last four decades in Austria. More cases can be expected in the foreseeable future due to changes in human behavior and (travel) activities as well as climate changes and the associated alterations in the availability of the natural reservoir, the vectors and the intrinsic characteristics of the parasite.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

BackgroundThe diagnosis of Sudeck's syndrome stage 1 (nowadays termed complex regional pain syndrome I, abbreviated CRPS I) is based on clinical features, namely swelling and pain in a limb. Plain X-ray may be normal. In the absence of pain sensitivity, e.g. in diabetic neuropathy, CRPS I of the foot can be mistaken for Charcot's foot stage 0 (so-called neuro-osteoarthropathy).Case presentationThe case of a type-1 diabetic woman is reported, in whom CRPS I following a calcaneal fracture was mistaken for Charcot's osteoarthropathy (because of bone marrow edema displayed by conventional MR imaging). In addition, a review is presented on 6 consecutive cases with CRPS I of the foot, and on 20 cases with Charcot's foot stage 0, with particular emphasis on MR imaging findings. The number of bones per foot affected with marrow edema was similar in either condition, with a tendency towards a more patchy, diffuse distribution of bone marrow edema in CRPS I. Bone marrow edema apparently regressed more promptly in response to treatment in Charcot's foot stage 0.ConclusionDifferentiation of CRPS I from Charcot's foot stage 0 remains a diagnostic dilemma in patients with pain insensitivity. Conventional MRI may be helpful, when repeated for monitoring the treatment response.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

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Background Chronic sun damage in the background is common in pigmented actinic keratoses and Bowen’s disease (pAK/BD). While explainable artificial intelligence (AI) demonstrated increased background attention for pAK/BD, humans frequently miss this clue in dermatoscopic images because they tend to focus on the lesion. Aim To analyse whether perilesional sun damage is a robust diagnostic clue for pAK/BD and if teaching this clue to dermatoscopy users improves their diagnostic accuracy. Methods We assessed the interrater agreement and the frequency of perilesional sun damage in 220 dermatoscopic images and conducted a reader study with 124 dermatoscopy users. The readers were randomly assigned to one of two online tutorials; one tutorial pointed to perilesional sun damage as a clue to pAK/BD (group A) the other did not (group B). In both groups, we compared the frequencies of correct diagnoses before and after receiving the tutorial. Results The frequency of perilesional sun damage was higher in pAK/BD than in other types of pigmented skin lesions and interrater agreement was good (kappa = 0.675). The diagnostic accuracy for pAK/BD improved in both groups of readers (group A: +16.1%, 95%‐CI: 9.5–22.7; group B: +13.1%; 95%‐CI: 7.1–19.0; P for both <0.001), but the overall accuracy improved only in group A from (59.1% (95%‐CI: 55.0–63.1) to 63.5% (95%‐CI: 59.5–67.6); P = 0.002). Conclusion Perilesional sun damage is a good clue to differentiate pAK/BD from other pigmented skin lesions in dermatoscopic images, which could be useful for teledermatology. Knowledge of this clue improves the accuracy of dermatoscopy users, which demonstrates that insights from explainable AI can be used to train humans.

Identifying genes and cellular pathways associated with normative brain physiology and behavior could help discover molecular therapies that target specific psychiatric symptoms with minimal side effects. Linking genotype-phenotype associations from population-scale datasets to brain function is challenging because of the multi-level, heterogeneous nature of brain organization. To address this challenge, we developed a novel brain-focused gene and pathway prioritization workflow, which maps variants to genes based on knowledge of brain genome regulation, and subsequently to pathways, cells, diseases and drugs (21 resources). We applied this workflow to nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (subset of 3,319 individuals aged 8-21 years). We report genome-wide significance of variants associated with nonverbal reasoning within the 3’ end of the FBLN1 gene (p=4.6×10-8), itself linked to fetal neurodevelopment and psychotic disorders. These findings suggest that nonverbal reasoning and FBLN1 variation warrant further investigation in studies of psychosis. Multiple cognitive tasks demonstrated significant enrichment of variants in cellular pathways and brain-related gene sets, such as organ development, cell proliferation and nervous system dysfunction. Top-ranking genes in working memory associated pathways are genetically associated with multiple diseases with working memory deficits, including schizophrenia and Parkinson’s disease, and with multiple drugs, suggesting that choice of therapy for memory deficits should consider disease context. Given the large amount of additional biological insight derived from our pathway analysis, versus a standard gene-based approach, we propose that “genes to behaviour” frameworks for modeling brain-related phenotypes, like RDoC, should include pathway information to create a “genes to pathways to behaviour” approach. Our workflow is broadly useful to put genotype-phenotype associations of brain-related phenotypes into the context of brain organization, function, disease and known molecular therapies.