Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study.

BACKGROUND: Preoperative assessment of whether a melanoma is invasive or in situ (MIS) is a common task that might have important implications for triage, prognosis and the selection of surgical margins. Several dermoscopic features suggestive of melanoma have been described, but only a few of these are useful in differentiating MIS from invasive melanoma. OBJECTIVE: The primary aim of this study was to evaluate how accurately a large number of international readers, individually as well as collectively, were able to discriminate between MIS and invasive melanomas as well as estimate the Breslow thickness of invasive melanomas based on dermoscopy images. The secondary aim was to compare the accuracy of two machine learning convolutional neural networks (CNNs) and the collective reader response. METHODS: We conducted an open, web-based, international, diagnostic reader study using an online platform. The online challenge opened on 10 May 2021 and closed on 19 July 2021 (71 days) and was advertised through several social media channels. The investigation included, 1456 dermoscopy images of melanomas (788 MIS; 474 melanomas ≤1.0 mm and 194 >1.0 mm). A test set comprising 277 MIS and 246 invasive melanomas was used to compare readers and CNNs. RESULTS: We analysed 22 314 readings by 438 international readers. The overall accuracy (95% confidence interval) for melanoma thickness was 56.4% (55.7%-57.0%), 63.4% (62.5%-64.2%) for MIS and 71.0% (70.3%-72.1%) for invasive melanoma. Readers accurately predicted the thickness in 85.9% (85.4%-86.4%) of melanomas ≤1.0 mm (including MIS) and in 70.8% (69.2%-72.5%) of melanomas >1.0 mm. The reader collective outperformed a de novo CNN but not a pretrained CNN in differentiating MIS from invasive melanoma. CONCLUSIONS: Using dermoscopy images, readers and CNNs predict melanoma thickness with fair to moderate accuracy. Readers most accurately discriminated between thin (≤1.0 mm including MIS) and thick melanomas (>1.0 mm).

Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease.

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.

Estimation of severe drug-drug interaction warnings by medical specialist groups for Austrian nationwide eMedication.

OBJECTIVE: The objective of this study is to estimate the amount of severe drug-drug interaction warnings per medical specialist group triggered by prescribed drugs of a patient before and after the introduction of a nationwide eMedication system in Austria planned for 2015. METHODS: The estimations of interaction warnings are based on patients' prescriptions of a single health care professional per patient, as well as all patients' prescriptions from all visited health care professionals. We used a research database of the Main Association of Austrian Social Security Organizations that contains health claims data of the years 2006 and 2007. RESULTS: The study cohort consists of about 1 million patients, with 26.4 million prescribed drugs from about 3,400 different health care professionals. The estimation of interaction warnings show a heterogeneous pattern of severe drug-drug-interaction warnings across medical specialist groups. CONCLUSION: During an eMedication implementation it must be taken into consideration that different medical specialist groups require customized support.

JADE: a tool for medical researchers to explore adverse drug events using health claims data.

OBJECTIVE: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays. METHODS: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database. RESULTS: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented. CONCLUSION: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.

Semantic validation of standard-based electronic health record documents with W3C XML schema.

OBJECTIVES: The goal of this article is to examine whether W3C XML Schema provides a practicable solution for the semantic validation of standard-based electronic health record (EHR) documents. With semantic validation we mean that the EHR documents are checked for conformance with the underlying archetypes and reference model. METHODS: We describe an approach that allows XML Schemas to be derived from archetypes based on a specific naming convention. The archetype constraints are augmented with additional components of the reference model within the XML Schema representation. A copy of the EHR document that is transformed according to the before-mentioned naming convention is used for the actual validation against the XML Schema. RESULTS: We tested our approach by semantically validating EHR documents conformant to three different ISO/EN 13606 archetypes respective to three sections of the CDA implementation guide "Continuity of Care Document (CCD)" and an implementation guide for diabetes therapy data. We further developed a tool to automate the different steps of our semantic validation approach. CONCLUSIONS: For two particular kinds of archetype prescriptions, individual transformations are required for the corresponding EHR documents. Otherwise, a fully generic validation is possible. In general, we consider W3C XML Schema as a practicable solution for the semantic validation of standard-based EHR documents.

Geomatics in injury prevention: the science, the potential and the limitations.

BACKGROUND: Geomatics describes the activities involved in acquiring and managing geographical data and producing geographical information for scientific, administrative and technical endeavors. As an emerging science, geomatics has a great potential to support public health. Geomatics provides a conceptual foundation for the development of geographic information systems (GIS), computerized tools that manage and display geographical data for analytical applications. As descriptive epidemiology typically involves the examination of person, place and time in the occurrence of disease or injury, geomatics and GIS can play an important role in understanding and preventing injury. AIM: This article provides a background to geomatics for those in the injury prevention field who are unfamiliar with spatial analysis. We hope to stimulate researchers and practitioners to begin to use geomatics to assist in the prevention of injury. METHODS: The authors illustrate the potential benefits and limitations of geomatics in injury prevention in a non-technical way through the use of maps and analysis. RESULTS: By analysing the location of patients treated for fall injuries in Central Toronto using GIS, some demographic and land use variables, such as household income, age, and the location of homeless shelters, were identified as explanatory factors for the spatial distribution. CONCLUSION: By supporting novel approaches to injury prevention, geomatics has a great potential for efforts to combat the burden of injury. Despite some limitations, those with an interest in injury prevention could benefit from this science.