Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report.

Purpose: The purpose of this study was to develop a deep learning algorithm for detecting and quantifying incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) and complete retinal pigment epithelium and outer retinal atrophy (cRORA) in optical coherence tomography (OCT) that generalizes well to data from different devices and to validate in an intermediate age-related macular degeneration (iAMD) cohort.

Methods: The algorithm comprised a domain adaptation (DA) model, promoting generalization across devices, and a segmentation model for detecting granular biomarkers defining iRORA/cRORA, which are combined into iRORA/cRORA segmentations. Manual annotations of iRORA/cRORA in OCTs from different devices in the MACUSTAR study (168 patients with iAMD) were compared to the algorithm's output.

Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography.

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images.

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.

Purpose: Significant visual impairment due to glaucoma is largely caused by the disease being detected too late.

Objective: To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG).

Design: Cross-sectional study.

A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography.

Purpose: The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans.

Methods: A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls.

Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison.

Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components.

Simulation exercises and after action reviews - analysis of outputs during 2016-2019 to strengthen global health emergency preparedness and response.

Background: Under the International Health Regulations (2005) [IHR (2005)] Monitoring and Evaluation Framework, after action reviews (AAR) and simulation exercises (SimEx) are two critical components which measure the functionality of a country's health emergency preparedness and response under a "real-life" event or simulated situation. The objective of this study was to describe the AAR and SimEx supported by the World Health Organization (WHO) globally in 2016-2019.

Methods: In 2016-2019, WHO supported 63 AAR and 117 SimEx, of which 42 (66.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI.

One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI.

Results from an observational trial: digital epiluminescence microscopy follow-up of atypical nevi increases the sensitivity and the chance of success of conventional dermoscopy in detecting melanoma.

We analyzed the value of digital epiluminescence microscopy (DELM) for the long-term follow-up of atypical nevi. Patients (n=530) were prospectively categorized into defined melanoma risk groups and followed by clinical and epiluminescence microscopy (ELM) examinations. Atypical nevi (n=7001) were additionally followed by DELM.

Speckled lentiginous nevus syndrome: report of a further case.

A 42-year-old man had a large speckled lentiginous nevus on the left side of his trunk. The involved area was painful when touched and paresthetic. Moreover, the ipsilateral half of his body showed a pronounced hyperhidrosis.

Results of a surveillance programme for patients at high risk of malignant melanoma using digital and conventional dermoscopy.

Patients with a high number of atypical naevi and a personal and/or family history of melanoma are at high risk of malignant melanoma. The objective of this study was to design a special documentation and surveillance programme using epiluminescence microscopy (ELM) and digital epiluminescence microscopy (DELM) to improve the surveillance of these patients. High-risk patients (n=212) were categorized by the number and phenotype of their naevi and their personal and family history of melanoma.

[Pyoderma gangrenosum: and important differential diagnosis from wound infection. Case report of a life threatening course].

Pyoderma gangrenosum (PG) is a necrotizing and ulcerative skin disease of unknown cause. The pathogenesis is thought to be related to a defective immune response. The ulcerations appear spontaneously or after skin trauma.

Erosive mucosal lichen planus: response to topical treatment with tacrolimus.

Erosive mucosal lichen planus is a painful and disabling inflammatory skin disease that is highly resistant to topical treatment. We report on six patients with severe recalcitrant erosive mucosal lichen planus who benefited from topical application of tacrolimus ointment. After 4 weeks of treatment, complete resolution was observed in three cases, and substantial improvement was achieved in the other three patients.

[Sclerosing in multiple familial glomangiomas].

Glomus tumors (glomangiomata) are benign tumors arising from glomus cells. Multiple glomangiomata are less frequent and less painful than the solitary variant, which is usually located subungually. Nonetheless multiple glomangiomata--sometimes being sensitive to pressure and changes in temperature--may cause considerable discomfort.

[Granuloma eosinophilicum faciei--successful cryosurgery treatment of 6 patients].

Six patients with granuloma faciale, including patients with multiple lesions, were treated successfully with cryosurgery. Granuloma faciale is known to be difficult to treat. Cryosurgery is an effective and minimally invasive therapy for this granulomatous inflammation of the skin.