Nodular lesions of the buttock for 20 years: the challenge of chromoblastomycosis in non-endemic settings.

Chromoblastomycosis is an implantation mycosis of the skin caused by certain species of melanised fungi. A man in his 50s, born in Kerala but living in England for 14 years, presented with a nodular lesion on his left buttock, which had been present for 20 years. Biopsy revealed muriform cells and fungal culture isolated Fonsecaea spp, consistent with a diagnosis of chromoblastomycosis. Treatment with oral terbinafine was initiated and changed to itraconazole based on results of antifungal susceptibility. Drug intolerance and low drug levels of itraconazole necessitated change to voriconazole and topical terbinafine. Despite long-term combined therapy, the lesions worsened, and the patient opted for surgical excision abroad. Recurrence was evident at surgical sites and combined therapy continues. Chromoblastomycosis is an insidious and burdensome neglected tropical disease. Within non-endemic countries, diagnosis remains challenging. A travel history and appropriate fungal investigations are vital.

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

Importance: A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services. Objective: To determine the accuracy of an artificial intelligence algorithm in identifying melanoma in dermoscopic images of lesions taken with smartphone and digital single-lens reflex (DSLR) cameras. Design, Setting, and Participants: This prospective, multicenter, single-arm, masked diagnostic trial took place in dermatology and plastic surgery clinics in 7 UK hospitals. Dermoscopic images of suspicious and control skin lesions from 514 patients with at least 1 suspicious pigmented skin lesion scheduled for biopsy were captured on 3 different cameras. Data were collected from January 2017 to July 2018. Clinicians and the Deep Ensemble for Recognition of Malignancy, a deterministic artificial intelligence algorithm trained to identify melanoma in dermoscopic images of pigmented skin lesions using deep learning techniques, assessed the likelihood of melanoma. Initial data analysis was conducted in September 2018; further analysis was conducted from February 2019 to August 2019. Interventions: Clinician and algorithmic assessment of melanoma. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the algorithmic and specialist assessment, determined using histopathology diagnosis as the criterion standard. Results: The study population of 514 patients included 279 women (55.7%) and 484 white patients (96.8%), with a mean (SD) age of 52.1 (18.6) years. A total of 1550 images of skin lesions were included in the analysis (551 [35.6%] biopsied lesions; 999 [64.4%] control lesions); 286 images (18.6%) were used to train the algorithm, and a further 849 (54.8%) images were missing or unsuitable for analysis. Of the biopsied lesions that were assessed by the algorithm and specialists, 125 (22.7%) were diagnosed as melanoma. Of these, 77 (16.7%) were used for the primary analysis. The algorithm achieved an AUROC of 90.1% (95% CI, 86.3%-94.0%) for biopsied lesions and 95.8% (95% CI, 94.1%-97.6%) for all lesions using iPhone 6s images; an AUROC of 85.8% (95% CI, 81.0%-90.7%) for biopsied lesions and 93.8% (95% CI, 91.4%-96.2%) for all lesions using Galaxy S6 images; and an AUROC of 86.9% (95% CI, 80.8%-93.0%) for biopsied lesions and 91.8% (95% CI, 87.5%-96.1%) for all lesions using DSLR camera images. At 100% sensitivity, the algorithm achieved a specificity of 64.8% with iPhone 6s images. Specialists achieved an AUROC of 77.8% (95% CI, 72.5%-81.9%) and a specificity of 69.9%. Conclusions and Relevance: In this study, the algorithm demonstrated an ability to identify melanoma from dermoscopic images of selected lesions with an accuracy similar to that of specialists.

Focal Hyperhidrosis Associated with Recurrent Urinary Tract Infections.

Hyperhidrosis affects almost 3% of the population and is characterized by sweating that occurs in excess of that needed for normal thermoregulation. It can occur as a primary disease or secondary to underlying clinical conditions. Hyperhidrosis can stem from neurogenic sympathetic over activity involving normal eccrine glands. We report the interesting case of a 75-year-old male patient with a 6-month history of new onset secondary focal hyperhidrosis of buttocks, pelvis, and upper thighs. Each time his symptoms worsened he was found to have culture positive urine samples for Escherichia coli (E. coli). He underwent urological investigation and was found to have urethral strictures and cystitis. The hyperhidrosis improved each time his urinary tract infection (UTI) was treated with antibiotics and continued to remain stable with a course of prophylactic trimethoprim. We hypothesize that the patient's urethral strictures led to inhibition in voiding which in turn increased the susceptibility to UTIs. Accumulation of urine and increased bladder pressure in turn raised sympathetic nerve discharge leading to excessive sweating. We recommend that a urine dip form part of the routine assessment of patients presenting with new onset focal hyperhidrosis of pelvis, buttocks, and upper thighs. Timely urological referral should be made for all male patients with recurrent UTI. To the authors' knowledge, there have been no other reports of UTI-associated focal hyperhidrosis.