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.
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).
GRB 110205A: ANATOMY OF A LONG GAMMA-RAY BURST GENDRE, B; ATTEIA, J. L; VACHIER, F ...
Astrophysical journal/The Astrophysical journal,
03/2012, Volume:
748, Issue:
1
Journal Article
Peer reviewed
Open access
The Swift burst GRB 110205A was a very bright burst visible in the Northern Hemisphere. GRB 110205A was intrinsically long and very energetic and it occurred in a low-density interstellar medium ...environment, leading to delayed afterglow emission and a clear temporal separation of the main emitting components: prompt emission, reverse shock, and forward shock. Our observations show several remarkable features of GRB 110205A: the detection of prompt optical emission strongly correlated with the Burst Alert Telescope light curve, with no temporal lag between the two; the absence of correlation of the X-ray emission compared to the optical and high-energy gamma-ray ones during the prompt phase; and a large optical re-brightening after the end of the prompt phase, that we interpret as a signature of the reverse shock. Beyond the pedagogical value offered by the excellent multi-wavelength coverage of a gamma-ray burst with temporally separated radiating components, we discuss several questions raised by our observations: the nature of the prompt optical emission and the spectral evolution of the prompt emission at high energies (from 0.5 keV to 150 keV); the origin of an X-ray flare at the beginning of the forward shock; and the modeling of the afterglow, including the reverse shock, in the framework of the classical fireball model.
The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support ...into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice.
Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art ...machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions.
For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms.
Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 SD 3·42 vs 19·92 4·27). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06–7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9–12·9 vs 3·6%, 0·8–6·3; p<0·0001).
State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research.
None.
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.
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.
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.
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.
Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and ...nonmelanocytic, and are more difficult to diagnose.
To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.
A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.
The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.
Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18).
Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.
Technologies associated with the second-generation of the World-Wide Web enable virtually anyone to share their data, documents, observations, and opinions on the Internet. In less than three years, ...mapping platforms such as Google Maps have sparked an exponential growth in user-generated geographically referenced content. However, the “serious” applications of Web 2.0 are sparse and this paper assesses its use in the context of collaborative spatial decision-making. We present an online map-based discussion forum that enables Internet users to submit place-based comments and respond to contributions from other participants. We further use the geographic references in a thread-based master plan debate for a university campus to simulate this debate in the map-based forum. This allows us to demonstrate how the online map provides an overview of the status and spatial foci of the debate, and how it can help us understand the spatial thought processes of the participants.
Nonpigmented skin cancer is common, and diagnosis with the unaided eye is error prone.
To investigate whether dermatoscopy improves the diagnostic accuracy for nonpigmented (amelanotic) cutaneous ...neoplasms.
We collected a sample of 2072 benign and malignant neoplastic lesions and inflammatory conditions and presented close-up images taken with and without dermatoscopy to 95 examiners with different levels of experience.
The area under the curve was significantly higher with than without dermatoscopy (0.68 vs 0.64, P < .001). Among 51 possible diagnoses, the correct diagnosis was selected in 33.1% of cases with and 26.4% of cases without dermatoscopy (P < .001). For experts, the frequencies of correct specific diagnoses of a malignant lesion improved from 40.2% without to 51.3% with dermatoscopy. For all malignant neoplasms combined, the frequencies of appropriate management strategies increased from 78.1% without to 82.5% with dermatoscopy.
The study deviated from a real-life clinical setting and was potentially affected by verification and selection bias.
Dermatoscopy improves the diagnosis and management of nonpigmented skin cancer and should be used as an adjunct to examination with the unaided eye.
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