Transforming Dermatologic Imaging for the Digital Era: Metadata and Standards

From data to diagnosis: skin cancer image datasets for artificial intelligence

Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for the evaluation of algorithm generalizability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritized groups in existing datasets are highlighted, which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques that may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.

A Narrative Review: Opportunities and Challenges in Artificial Intelligence Skin Image Analyses Using Total Body Photography

Artificial intelligence (AI) algorithms for skin lesion classification have reported accuracy at par with and even outperformance of expert dermatologists in experimental settings. However, the majority of algorithms do not represent real-world clinical approach where skin phenotype and clinical background information are considered. We review the current state of AI for skin lesion classification and present opportunities and challenges when applied to total body photography (TBP). AI in TBP analysis presents opportunities for intrapatient assessment of skin phenotype and holistic risk assessment by incorporating patient-level metadata, although challenges exist for protecting patient privacy in algorithm development and improving explainable AI methods.

Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.

Prediction of melanoma Breslow thickness using deep transfer learning algorithms

BACKGROUND

The distinction between in situ melanoma (MIS) and invasive melanoma is challenging even for expert dermatologists. The use of pretrained convolutional neural networks (CNNs) as ancillary decision systems needs further research.

AIM

To develop, validate and compare three deep transfer learning (DTL) algorithms to predict MIS vs. invasive melanoma and melanoma with a Breslow thickness (BT) of < 0.8 mm vs. ≥ 0.8 mm.

METHODS

A dataset of 1315 dermoscopic images of histopathologically confirmed melanomas was created from Virgen del Rocio University Hospital and open repositories of the International Skin Imaging Collaboration archive and Polesie S et al. (Dermatol Pract Concept 2021; 11:e2021079). The images were labelled as MIS or invasive melanoma and < 0.8 mm or ≥ 0.8 mm of BT. We conducted three trainings, and overall means for receiver operating characteristic (ROC) curves, sensitivity, specificity, positive and negative predictive value, and balanced diagnostic accuracy outcomes were evaluated on the test set with ResNetV2, EfficientNetB6 and InceptionV3. The results of 10 dermatologists were compared with the algorithms. Grad-CAM gradient maps were generated, highlighting relevant areas considered by the CNNs within the images.

RESULTS

EfficientNetB6 achieved the highest diagnostic accuracy for the comparison between MIS vs. invasive melanoma (61%) and BT < 0.8 mm vs. ≥ 0.8 mm (75%). For the BT comparison, ResNetV2 with an area under the ROC curve of 0.76 and InceptionV3 with an area under the ROC curve of 0.75, outperformed the results obtained by the dermatologist group with an area under the ROC curve of 0.70.

CONCLUSION

EfficientNetB6 recorded the best prediction results, outperforming the dermatologists for the comparison of 0.8 mm of BT. DTL could be an ancillary aid to support dermatologists' decisions in the near future.

A survey on deep learning for skin lesion segmentation

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online³.

Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Cornea topography maps allow ophthalmologists to screen and diagnose cornea pathologies. We aim to automatically identify any cornea abnormalities based on such cornea topography maps, with focus on diagnosing keratoconus. To do so, we represent the OCT scans as images and apply Convolutional Neural Networks (CNNs) for the automatic analysis. The model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. A set of 1940 consecutive screening scans from the Saarland University Hospital Clinic for Ophthalmology was annotated and used for model training and validation. All scans were recorded with a CASIA2 anterior segment Optical Coherence Tomography (OCT) scanner. The proposed model achieves a sensitivity of 98.46% and a specificity of 91.96% when distinguishing between healthy and pathological corneas. Our approach enables the screening of cornea pathologies and the classification of common pathologies like keratoconus. Furthermore, the approach is independent of the topography scanner and enables the visualization of those scan regions which drive the model's decisions.

HI-MViT: A lightweight model for explainable skin disease classification based on modified MobileViT

Objective

To develop an explainable lightweight skin disease high-precision classification model that can be deployed to the mobile terminal.

Methods

In this study, we present HI-MViT, a lightweight network for explainable skin disease classification based on Modified MobileViT. HI-MViT is mainly composed of ordinary convolution, Improved-MV2, MobileViT block, global pooling, and fully connected layers. Improved-MV2 uses the combination of shortcut and depth classifiable convolution to substantially decrease the amount of computation while ensuring the efficient implementation of information interaction and memory. The MobileViT block can efficiently encode local and global information. In addition, semantic feature dimensionality reduction visualization and class activation mapping visualization methods are used for HI-MViT to further understand the attention area of the model when learning skin lesion images.

Results

The International Skin Imaging Collaboration has assembled and made available the ISIC series dataset. Experiments using the HI-MViT model on the ISIC-2018 dataset achieved scores of 0.931, 0.932, 0.961, and 0.977 on F1-Score, Accuracy, Average Precision (AP), and area under the curve (AUC). Compared with the top five algorithms of ISIC-2018 Task 3, Marco's average F1-Score, AP, and AUC have increased by 6.9%, 6.8%, and 0.8% compared with the suboptimal performance model. Compared with ConvNeXt, the most competitive convolutional neural network architecture, our model is 5.0%, 3.4%, 2.3%, and 2.2% higher in F1-Score, Accuracy, AP, and AUC, respectively. The experiments on the ISIC-2017 dataset also achieved excellent results, and all indicators were better than the top five algorithms of ISIC-2017 Task 3. Using the trained model to test on the PH2 dataset, an excellent performance score is obtained, which shows that it has good generalization performance.

Conclusions

The skin disease classification model HI-MViT proposed in this article shows excellent classification performance and generalization performance in experiments. It demonstrates how the classification outcomes can be applied to dermatologists' computer-assisted diagnostics, enabling medical professionals to classify various dermoscopic images more rapidly and reliably.

DICODerma: A Practical Approach for Metadata Management of Images in Dermatology

Clinical images are vital for diagnosing and monitoring skin diseases, and their importance has increased with the growing popularity of machine learning. Lack of standards has stifled innovation in dermatological imaging, unlike other image-intensive specialties such as radiology. We investigate the meta-requirements for utilizing the popular DICOM standard for metadata management of images in dermatology. We propose practical design solutions and provide open-source tools to integrate dermatologists’ workflow with enterprise imaging systems. Using the tool, dermatologists can tag, search, organize and convert clinical images to the DICOM format. We believe that our less disruptive approach will improve the adoption of standards in the specialty.

The Future of Precision Prevention for Advanced Melanoma

Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.

Artificial intelligence in dermatology

Dermatology and medicine are producing data at an increasing rate that are progressively difficult to sort and manage. Artificial intelligence (AI) and machine learning are examples of tools that may have the capability to produce significant and meaningful results from these data. Currently, AI and machine learning have a variety of applications in medicine including, but not limited to, diagnostics, patient management, preventive medicine, and genomic analysis. Although the role of AI in dermatology is greater than ever, its use is still extremely limited. As AI is continually developed and implemented, it is essential that stakeholders understand AI terminology, applications, limitations, and projected uses in dermatology. With the continued development of AI technology, however, its implementation may afford greater dermatologist efficiency, greater increased patient access to dermatologic care, and improved patient outcomes.

Characteristics of publicly available skin cancer image datasets: a systematic review

Publicly available skin image datasets are increasingly used to develop machine learning algorithms for skin cancer diagnosis. However, the total number of datasets and their respective content is currently unclear. This systematic review aimed to identify and evaluate all publicly available skin image datasets used for skin cancer diagnosis by exploring their characteristics, data access requirements, and associated image metadata. A combined MEDLINE, Google, and Google Dataset search identified 21 open access datasets containing 106 950 skin lesion images, 17 open access atlases, eight regulated access datasets, and three regulated access atlases. Images and accompanying data from open access datasets were evaluated by two independent reviewers. Among the 14 datasets that reported country of origin, most (11 [79%]) originated from Europe, North America, and Oceania exclusively. Most datasets (19 [91%]) contained dermoscopic images or macroscopic photographs only. Clinical information was available regarding age for 81 662 images (76·4%), sex for 82 848 (77·5%), and body site for 79 561 (74·4%). Subject ethnicity data were available for 1415 images (1·3%), and Fitzpatrick skin type data for 2236 (2·1%). There was limited and variable reporting of characteristics and metadata among datasets, with substantial under-representation of darker skin types. This is the first systematic review to characterise publicly available skin image datasets, highlighting limited applicability to real-life clinical settings and restricted population representation, precluding generalisability. Quality standards for characteristics and metadata reporting for skin image datasets are needed.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Government Big Data Ecosystem: Definitions, Types of Data, Actors, and Roles and the Impact in Public Administrations

The public sector, private firms, business community, and civil society are generating data that are high in volume, veracity, and velocity and come from a diversity of sources. This type of data is today known as big data. Public administrations pursue big data as “new oil” and implement data-centric policies to collect, generate, process, share, exploit, and protect data for promoting good governance, transparency, innovative digital services, and citizens’ engagement in public policy. All of the above constitute the Government Big Data Ecosystem (GBDE). Despite the great interest in this ecosystem, there is a lack of clear definitions, the various important types of government data remain vague, the different actors and their roles are not well defined, while the impact in key public administration sectors is not yet deeply understood and assessed. Such research and literature gaps impose a crucial obstacle for a better understanding of the prospects and nascent issues in exploiting GBDE. With this study, we aim to start filling the above-mentioned gaps by organizing our findings from an extended Systematic Literature Review into a framework to organise and address the above-mentioned challenges. Our goal is to contribute in this fast-evolving area by bringing some clarity and establishing common understanding around key elements of the emerging GBDE.

Development of a Checklist Tool to Assess the Quality of Skin Lesion Images Acquired by Consumers Using Sequential Mobile Teledermoscopy

BACKGROUND

Mobile teledermoscopy is an emerging technology that involves imaging and digitally sending dermoscopic images of skin lesions to a clinician for assessment. High-quality, consistent images are required for accurate telediagnoses when monitoring lesions over time. To date there are no tools to assess the quality of sequential images taken by consumers using mobile teledermoscopy. The purpose of this study was to develop a tool to assess the quality of images acquired by consumers.

METHODS

Participants imaged skin lesions that they felt were concerning at baseline, 1-, and 2-months. A checklist to assess the quality of consumer sequential imaging of skin lesions was developed based on the International Skin Imaging Collaboration guidelines. A scale was implemented to grade the quality of the images: 0 (low) to 18 (very high). Intra- and inter-reliability of the checklist was assessed using Bland-Altman analysis. Using this checklist, the consistency with which 85 sets of images were scored by 2 evaluators were compared using Kappa statistics. Items with a low Kappa value <0.4 were removed.

RESULTS

After reliability testing, 5 of the items were removed due to low Kappa values (<0.4) and the final checklist included 13 items surveying: lesion selection; image orientation; lighting; field of view; focus and depth of view. Participants had a mean age of 41 years (range 19-73), and 67% were female. Most participants (84%, n = 71/85) were able to select and image the correct lesion over time for both the dermoscopic and overview images. Younger participants (<40 years old) scored significantly higher (8.1 ± 2.1) on the imaging checklist compared to older participants (7.1 ± 2.4; p = 0.037). Participants had most difficulty with consistent image orientation.

CONCLUSIONS

This checklist could be used as a triage tool to filter images acquired by consumers prior to telediagnosis evaluation, which would improve the efficiency and accuracy of teledermatology and teledermoscopy processes. It may also be used to provide feedback to the consumers to improve image acquisition over time.

Supporting Virtual Dermatology Consultation in the Setting of COVID-19

While telemedicine has been utilized with more frequency over the past two decades, there remained significant barriers to its broad implementation. The COVID-19 global pandemic served as a stimulus for rapid expansion and implementation of telemedicine services across medical institutions worldwide in order to maximize patient care delivery, minimize exposure risk among healthcare providers and patients alike, and avoid overcrowding of patient care facilities. In this experience report, we highlight the teledermatology initiatives executed by the Dermatology Service at Memorial Sloan Kettering Cancer Center in New York City, with particular emphasis on image ingestion and potential for future automation and improvement.

The Role of DICOM in Artificial Intelligence for Skin Disease

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

A patient-centric dataset of images and metadata for identifying melanomas using clinical context

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.

Recent developments in dermoscopy for dermatology

BACKGROUND

Dermoscopy is considered to be a bridge between clinical observation and histopathological examination, allowing the in vivo examination of skin microstructures that are not visible to the naked eye, from the epidermis to the superficial dermis. Dermoscopy has undergone rapid development, witnessing the history from natural light to polarized light, from handheld dermoscopy to videodermoscopy, and from classic dermoscopy to digital dermoscopy. Its application extends from the initial differential diagnosis of pigmented skin diseases (melanocytic and nonmelanocytic) to general dermatology, including appendage (nail and hair) abnormalities and diseases related to infection and inflammation.

AIMS

We aimed to provide the latest developments in dermoscopy from the perspective of handheld dermoscopy, videodermoscopy, fluorescence-advanced videodermatoscopy, polarized transilluminating dermoscopy, and digital dermoscopy.

METHODS

In this review, we searched the PubMed, Embase, Web of Science, and Cochrane Library databases for reviews, case reports, and observational studies on dermoscopy.

RESULTS

We provided an updated review of dermoscopy based on published literature.

CONCLUSION

Dermoscopy is an indispensable diagnostic tool in dermatology, and it is expected to be further developed in the future.

Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration

BACKGROUND

There is no internationally vetted set of anatomic terms to describe human surface anatomy.

OBJECTIVE

To establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy.

METHODS

We conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in seven levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered 'accepted' and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with <75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms.

RESULTS

The Delphi included 21 participants. We found consensus (≥75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published International Classification of Diseases-Surface Topography list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method.

CONCLUSIONS

We have established a set of 513 clinically relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.

A review of literature supporting the development of practice guidelines for teledermatology in Australia

Despite the potential of teledermatology to increase access to dermatology services and improve patient care, it is not widely practised in Australia. In an effort to increase uptake of teledermatology, Australian-specific practice guidelines for teledermatology are being developed by the Australasian College of Dermatologist. This paper reports finding from literature reviews that were undertaken to inform the development of these guidelines. Results cover the following sections: Modalities of teledermatology; Patient selection and consent; Imaging; Quality and safety; Privacy and security; Communication; and Documentation and retention. The document educates providers about the benefits and limitations of telehealth while articulating how to enhance patient care and reduce risk when practicing teledermatology.

Clinical Perspective of 3D Total Body Photography for Early Detection and Screening of Melanoma

Melanoma incidence continues to increase across many populations globally and there is significant mortality associated with advanced disease. However, if detected early, patients have a very promising prognosis. The methods that have been utilized for early detection include clinician and patient skin examinations, dermoscopy (static and sequential imaging), and total body photography via 2D imaging. Total body photography has recently witnessed an evolution from 2D imaging with the ability to now create a 3D representation of the patient linked with dermoscopy images of individual lesions. 3D total body photography is a particularly beneficial screening tool for patients at high risk due to their personal or family history or those with multiple dysplastic naevi—the latter can make monitoring especially difficult without the assistance of technology. In this perspective, we discuss clinical examples utilizing 3D total body photography, associated advantages and limitations, and future directions of the technology. The optimal system for melanoma screening should improve diagnostic accuracy, be time and cost efficient, and accessible to patients across all demographic and socioeconomic groups. 3D total body photography has the potential to address these criteria and, most importantly, optimize crucial early detection.

Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products.