Automated Image Quality and Protocol Adherence Assessment of Examinations in Teledermatology: First Results.

Introduction: Image quality and acquisition protocol adherence assessment is a neglected area in teledermatology. We examine if it is feasible to use deep learning methods to automate the assessment of the adherence of examinations to image acquisition protocols. In this study, we focused on the quality criteria of two image acquisition protocols: (1) approximation image and (2) panoramic image, as these are present in all teledermatology examination protocols currently used by the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC). Methods: We use a data set of 36,102 teledermatological examinations performed at the STT/SC during 2021. As our validation process, we adopted standard machine learning metrics and an inter-rater agreement (IRA) study with 11 dermatologists. For the approximation image protocol, we used the Mask-Region based Convolutional Neural Network (RCNN) Object Detection Deep Learning (DL) architecture to identify the presence of a lesion identification tag and a ruler used to provide a frame reference of the lesion. For the panoramic image protocol, we used DensePose, a pose estimation DL, architecture to assess the presence of a whole patient body and its orientation. A combination of the two approaches was additionally validated through an IRA study between specialists. Results: Mask-RCNN achieved a score of 96% mean average precision (mAP), while DensePose presented 75% mAP. IRA achieved a level of agreement of 96.68% with the Krippendorff alpha score. Conclusions: Our results show the feasibility of using deep learning to automate the image quality and protocol adherence assessment in teledermatology, before the specialist's manual analysis of the examination.

What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review.

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.

Teleconsultations at a Pediatrics Outpatient Service in COVID-19 Pandemic: First Results.

Objective: Describe our experiences using teleconsultation approach to care for pediatric subspecialty follow-up patients during pandemic period. Methods: Synchronous teleconsultation solution was developed and implemented as a multiplatform/multimodality service, capable or running on desktop browsers and smartphones, and capable to handle chat, audio, and video. Term of consent was applied. Absolute number and percentage of patients assisted, as well as the form of consultation and the mean of attendance per patient were presented. Results: A telemedicine service was started using tools provided by The Santa Catarina State Integrated Telemedicine and Telehealth System offering real-time chat, through an online-based teleconsultation service. We assisted 75 patients in 109 consultations in 3 months, of which 69.7% were performed by chat. Mean of consultations per patient was 1.45. Conclusion: To our knowledge this is the first service in offering this modality of medicine for Brazilian public health care system. Patients and health care personnel reported satisfaction.

Creating a Web Infrastructure for the Support of Clinical Protocols and Clinical Management: An Example in Teledermatology.

Introduction: We describe a teledermatology infrastructure, implemented as part of a statewide large-scale telemedicine network, designed to provide comprehensive support for examination and clinical management protocols, which includes telediagnostic and patient triage and referral. It was implemented as a web-based system and an associated mobile application that supports both the primary healthcare facility team and the specialist during the patient care process. Methods: We describe the process models, protocols and technology employed, and the requirements generated for their development. We also present the results and experiences gained in implementing the model. The teledermatology service was implemented in 313 primary healthcare facilities in 286 municipalities in the State of Santa Catarina, Brazil. This study covers the period from January 2014 to June, 2018. Results: During this period, the teledermatology infrastructure processed 83,100 teledermatology examinations, of which 75,832 were validated and employed for patient triage and clinical management. Teledermatology allowed 33,112 patients to avoid further referral and be treated locally. Of this cohort, 7,513 patients presented more complex dermatoses that could be treated at the primary care level using telehealth-supported clinical management. Conclusions: Results indicate that this model contributes not only to the resolution of cases at the primary care level but also to the progressive improvement of the technical quality of dermatological examinations performed by technical staff at the primary healthcare.

GISTelemed: an online-based GIS approach to epidemiological analysis in telemedicine systems.

Large-scale telemedicine systems provide extensive amounts of data that can be used to gather epidemiological information. Epidemiologists have been using GIS systems for the easy and quick visualization of data and to perform georeferenced epidemiological analysis. This paper presents GISTelemed, a georeferenced epidemiological analysis tool developed as part of the Santa Catarina State Integrated Telemedicine and Telehealth System (STT/SC), a statewide telemedicine infrastructure in Brazil. The GISTelemed module offers an architecture supporting real-time recovery, information visualization and epidemiological analysis from structured and semi-structured data. The architecture uses controlled vocabularies for data catalogization and a specially developed ETL process that allows sending and receiving data on a large number of protocols, including DICOM SR and SQL. We performed a case study with users that indicates good perceived ease of use and usefulness of GISTelemed by both medical staff and health care managers.

Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution.

In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm2.

Does the use of structured reporting improve usability? A comparative evaluation of the usability of two approaches for findings reporting in a large-scale telecardiology context.

One of the main reasons that leads to a low adoption rate of telemedicine systems is poor usability. An aspect that influences usability during the reporting of findings is the input mode, e.g., if a free-text (FT) or a structured report (SR) interface is employed. The objective of our study is to compare the usability of FT and ST telemedicine systems, specifically in terms of user satisfaction, efficiency and general usability. We comparatively evaluate the usability of these two input modes in a telecardiology system for issuing electrocardiography reports in the context of a statewide telemedicine system in Brazil with more than 350.000 performed tele-electrocardiography examinations. We adopted a multiple method research strategy, applying three different kinds of usability evaluations: user satisfaction was evaluated through interviews with seven medical professionals using the System Usability Scale (SUS) questionnaire and specific questions related to adequacy and user experience. Efficiency was evaluated by estimating execution time using the Keystroke-Level Model (KLM). General usability was assessed based on the conformity of the systems to a set of e-health specific usability heuristics. The results of this comparison provide a first indication that a structured report (SR) input mode for such a system is more satisfactory and efficient with a larger conformity to usability heuristics than free-text (FT) input. User satisfaction using the SUS questionnaire has been scored in average with 58.8 and 77.5 points for the FT and SR system, respectively, which means that the SR system was rated 18.65 points higher than the FT system. In terms of efficiency, the completion of a findings report using the SR mode is estimated to take 8.5s, 3.74 times faster than using the FT system (31.8s). The SR system also demonstrated less violations to usability heuristics (8 points) in comparison to 14 points observed in the FT system. These results provide a first indication that the usage of structured reporting as an input mode in telecardiology systems may enhance usability. This also seems to confirm the advantages of the usage of structured reporting, as already described in the literature for other areas such as teleradiology.

Fundus image mosaicking for information augmentation in computer-assisted slit-lamp imaging.

Laser photocoagulation is currently the standard treatment for sight-threatening diseases worldwide, namely diabetic retinopathy and retinal vein occlusions. The slit lamp biomicroscope is the most commonly used device for this procedure, specially for the treatment of the eye periphery. However, only a small portion of the retina can be visualized through the biomicroscope, complicating the task of localizing and identifying surgical targets, increasing treatment duration and patient discomfort. In order to assist surgeons, we propose a method for creating intraoperative retina maps for view expansion using a slit-lamp device. Based on the mosaicking method described by Richa et al, 2012, the proposed method is a combination of direct and feature-based methods, suitable for the textured nature of the human retina. In this paper, we describe three major enhancements to the original formulation. The first is a visual tracking method using local illumination compensation to cope with the challenging visualization conditions. The second is an efficient pixel selection scheme for increased computational efficiency. The third is an entropy-based mosaic update method to dynamically improve the retina map during exploration. To evaluate the performance of the proposed method, we conducted several experiments on human subjects with a computer-assisted slit-lamp prototype. We also demonstrate the practical value of the system for photo documentation, diagnosis and intraoperative navigation.

DCMDSM: a DICOM decomposed storage model.

OBJECTIVE: To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects. METHODS: The proposal adapts the original decomposed storage model, incorporating structural and organizational characteristics present in DICOM image files. Tag values are stored according to their data types/domains, in a schema built on top of a standard relational database management system (RDBMS). Evaluation includes storing heterogeneous DICOM images, querying metadata using a variable number of predicates, and retrieving full-content images for different hierarchical levels. RESULTS AND DISCUSSION: When compared to a well established DICOM image archive, the proposal is 0.6-7.2 times slower in storing content; however, in querying individual tags, it is about 48.0% faster. In querying groups of tags, DICOM decomposed storage model (DCMDSM) is outperformed in scenarios with a large number of tags and low selectivity (being 66.5% slower); however, when the number of tags is balanced with better selectivity predicates, the performance gains are up to 79.1%. In executing full-content retrieval, in turn, the proposal is about 48.3% faster. CONCLUSIONS: DCMDSM is a model built for the storage of heterogeneous DICOM content, based on a straightforward database design. The results obtained through its evaluation attest its suitability as a storage layer for projects where DICOM images are stored once, and queried/retrieved whenever necessary.

Optimization of PACS data persistency using indexed hierarchical data.

We present a new approach for the development of a data persistency layer for a Digital Imaging and Communications in Medicine (DICOM)-compliant Picture Archiving and Communications Systems employing a hierarchical database. Our approach makes use of the HDF5 hierarchical data storage standard for scientific data and overcomes limitations of hierarchical databases employing inverted indexing for secondary key management and for efficient and flexible access to data through secondary keys. This inverted indexing is achieved through a general purpose document indexing tool called Lucene. This approach was implemented and tested using real-world data against a traditional solution employing a relational database, in various store, search, and retrieval experiments performed repeatedly with different sizes of DICOM datasets. Results show that our approach outperforms the traditional solution on most of the situations, being more than 600 % faster in some cases.

Implementing DICOM structured reporting in a large-scale telemedicine network.

INTRODUCTION: Large-scale asynchronous telemedicine networks can offer a unique opportunity for the acquisition of detailed epidemiological information if the data are acquired and handled in an appropriate way. In this work, an approach is presented for the integration of medical reports in the Digital Imaging and Communications in Medicine (DICOM) Structured Reporting standard in telemedicine networks using structured vocabularies. MATERIALS AND METHODS: The use of these structured vocabularies is extended beyond radiology, and a case study in telecardiology is presented. The approach was applied in the context of a real-world statewide public telemedicine network; nowadays on average 470 written electrocardiographic structured reports daily are being performed. Cardiologists provided more than 220,000 written structured reports, and these reports are stored into a central database. RESULTS: This study was performed during a 12-month period, and it was possible to examine possible associations between a list of co-morbidities and cardiac risk factors with a diagnosis that indicates the presence of cardiac ischemia, cardiac injury, or possible necrosis by using DICOM Structured Reporting. Our application is responsible for coordinating the process of issuance of reports through various technologies and devices. The system works as a library in an HTTP server, which accesses information from studies in DICOM format from the database and from structured vocabularies. CONCLUSIONS: Results indicate that traceability of morbidity, diagnoses, and patient clinical information can be achieved, resulting in an efficient data mining-friendly framework. A multidevice application for Web-based and smartphone-based platforms showed that it is a viable solution for applying the DICOM Structured Reporting standard in telemedicine networks.

Emissão de laudos eletrocardiográficos a distância: experiência da rede catarinense de telemedicina / Issuing electrocardiographic reports remotely: experience of the telemedicine network of Santa Catarina

FUNDAMENTO: Cresce o uso da Telemedicina, especialmente no envio e na avaliação de eletrocardiograma (ECG). É um procedimento de baixo custo, com alto potencial de salvar vidas. OBJETIVO: Descrever a forma de análise sistemática e o perfil do usuário da Rede Catarinense de Telemedicina quando o laudo era emitido de forma livre. MÉTODOS: Estudo observacional, transversal, determinando as associações entre características dos pacientes e diagnósticos eletrocardiográficos emitidos, dentre os usuários da Rede Catarinense de Telemedicina quando o laudo era fornecido de forma livre. Esse sistema estava conectado a 287 cidades de Santa Catarina, os exames eram feitos nos locais de origem e emitidos os laudos em três hospitais terciários. Entre 2005 e 2010, os laudos eram emitidos de forma livre e foi criado um método probabilístico para análise dos dados. Um cardiologista experiente avaliou todos os ECG para aferir a chance de anormalidade. RESULTADOS: Foram avaliados 243.363 ECG. A maioria (58%) foi realizada em pacientes com mais de 50 anos e proveniente da atenção primária (72%). Houve diferenças de frequência por região, parcialmente relacionado com número de cardiologistas/região (r = -0,551), com a distância dos centros terciários e com possíveis diferenças de aceitação do método. Cerca de 53% do ECG foram anormais, com maior frequência quanto maior a idade (r2 = 0,8166), e com diferenças regionais também significantes (p < 0,0001). CONCLUSÃO: Foi construído um sistema de análise dos dados integrando termos prevalentes, análise probabilística e dicionários especializados. O sistema tem atendido uma parcela significativa da população catarinense, principalmente idosos, da rede básica e de regiões remotas do estado.

BACKGROUND: A growing use of telemedicine has been observed, especially as regards the sending and evaluation of electrocardiograms (ECG); this is a low-cost procedure with a high potential to save lives. OBJECTIVES: To describe the form of systematic analysis and user profile of the Telemedicine Network of Santa Catarina during the time when the report was issued freely. METHODS: Observational cross-sectional study determining the associations between patient characteristics and electrocardiographic diagnoses issued among users of the Telemedicine Network of Santa Catarina during the time when the report was issued freely. This system was connected to 287 cities in Santa Catarina; the tests were done in the places of origin and the reports were issued in three tertiary-care hospitals. From 2005 to 2010 the reports were issued freely and a probabilistic method for data analysis was created. An experienced cardiologist evaluated all ECGs to assess the chances of abnormality. RESULTS: 243,363 ECGs were evaluated. The majority (58%) was performed on patients older than 50 years from primary care services (72%). There were differences in the frequency per region; this was partly related to the number of cardiologists/region (r = -0.551), to the distance from tertiary-care centers and potential differences of acceptance of the method. Approximately 53% of the ECGs were abnormal with greater frequency with increasing age (r² = 0.8166) and with significant regional differences (p < 0.0001). CONCLUSIONS: We built a data analysis system integrating prevalent terms, probabilistic analysis and specialized dictionaries. The system has covered a significant portion of the population of Santa Catarina, mainly elderly patients from the network of primary healthcare centers and remote regions of the State.

Issuing electrocardiographic reports remotely: experience of the telemedicine network of Santa Catarina.

BACKGROUND: A growing use of telemedicine has been observed, especially as regards the sending and evaluation of electrocardiograms (ECG); this is a low-cost procedure with a high potential to save lives. OBJECTIVES: To describe the form of systematic analysis and user profile of the Telemedicine Network of Santa Catarina during the time when the report was issued freely. METHODS: Observational cross-sectional study determining the associations between patient characteristics and electrocardiographic diagnoses issued among users of the Telemedicine Network of Santa Catarina during the time when the report was issued freely. This system was connected to 287 cities in Santa Catarina; the tests were done in the places of origin and the reports were issued in three tertiary-care hospitals. From 2005 to 2010 the reports were issued freely and a probabilistic method for data analysis was created. An experienced cardiologist evaluated all ECGs to assess the chances of abnormality. RESULTS: 243,363 ECGs were evaluated. The majority (58%) was performed on patients older than 50 years from primary care services (72%). There were differences in the frequency per region; this was partly related to the number of cardiologists/region (r = -0.551), to the distance from tertiary-care centers and potential differences of acceptance of the method. Approximately 53% of the ECGs were abnormal with greater frequency with increasing age (r(2) = 0.8166) and with significant regional differences (p < 0.0001). CONCLUSIONS: We built a data analysis system integrating prevalent terms, probabilistic analysis and specialized dictionaries. The system has covered a significant portion of the population of Santa Catarina, mainly elderly patients from the network of primary healthcare centers and remote regions of the State.

User satisfaction with asynchronous telemedicine: a study of users of Santa Catarina's system of telemedicine and telehealth.

User satisfaction analyses in synchronous telemedicine and teleconsultation environments have been widely performed and generally show satisfied users. In the field of asynchronous telemedicine, however, satisfaction studies were performed only in one single location or with a restricted set of users. With the aim of offering an exemplar evaluation of the impact of the statewide use of a large-scale asynchronous telemedicine network on the satisfaction of the involved users, this study presents the results obtained from a survey of the perceived quality of the service by both patients and healthcare staff. For this purpose, a survey with satisfaction questionnaires was performed with 564 patients from seven upstate municipalities and 56 healthcare professionals from 46 municipalities, using a methodology from the process improvement field. The collected data were quantified and underwent statistical analysis, which showed a clear perception of the improvement in the quality of service by both patients and healthcare professionals. The present findings also showed that both patients and healthcare professionals felt that introducing these new technologies was a positive step, even in upstate areas and when they involved great changes in the usual processes of primary care.

Performing real-time interactive fiber tracking.

Fiber tracking is a technique that, based on a diffusion tensor magnetic resonance imaging dataset, locates the fiber bundles in the human brain. Because it is a computationally expensive process, the interactivity of current fiber tracking tools is limited. We propose a new approach, which we termed real-time interactive fiber tracking, which aims at providing a rich and intuitive environment for the neuroradiologist. In this approach, fiber tracking is executed automatically every time the user acts upon the application. Particularly, when the volume of interest from which fiber trajectories are calculated is moved on the screen, fiber tracking is executed, even while it is being moved. We present our fiber tracking tool, which implements the real-time fiber tracking concept by using the video card's graphics processing units to execute the fiber tracking algorithm. Results show that real-time interactive fiber tracking is feasible on computers equipped with common, low-cost video cards.

Reconhecimento e quantificação de expressão de imunoistoquímica empregando aprendizado de métricas de distância / Recognition and quantification of immunohistochemistry expression distance metric learning

Este artigo apresenta uma abordagem de segmentação para o reconhecimento e quantificação de expressão de imunoistoquímica (IHC) através do aprendizado de uma métrica de distância. Este método é baseado em duas etapas: treinamento e segmentação. A etapa de treinamento é realizada pela seleção supervisionada de algumas áreas típicas de expressão de IHC na imagem. Nesta etapa o padrão esperado de IHC é estatisticamente caracterizado, onde ocorre o aprendizado da métrica de distância e um espaço característico é modelado. Através desse espaço são obtidos os mapas de similaridade para cada imagem de IHC, com os níveis de intensidade correspondendo ao grau da reação do biomarcador sobre o tecido. A etapa de segmentação é guiada por um parâmetro de escala que controla a quantidade de áreas marcadas com base nos valores de intensidade dos mapas de similaridade. O método é baseado no aprendizado da distância de Mahalanobis para produzir um espaço característico, para posteriormente ser utilizado na distinção entre marcações positivas de expressão de IHC e tecidos normais, bem como quantificar o grau de intensidade da reação. Os resultados obtidos pelo método proposto foram comparados com a classificação linear no espaço de cores HSV (Hue, Saturation and Value) utilizando diferentes categorias de biomarcadores. Os resultados mostram que os limites da fronteira da distribuição dos padrões são mais bem definidos no método proposto, permitindo assim uma melhor discriminação entre tecidos normais e expressão de IHC.

This paper presents a segmentation approach to the recognition and quantification of immunohistochemistry (IHC) expression employing a distance metric learning method. This method is based in a two-step procedure, training and segmentation. The training step is performed by the supervised selection of a few IHC typical stained areas on image. In that step the desired IHC pattern is statistically characterized, where a distance metric is learned and a featured space is created. With this space, similarity maps are obtained by each IHC image with its intensity levels corresponding to degrees of reaction provided by the biomarker over the tissue. The segmentation step is guided by a scale-space parameter that controls the amount of labeled areas based on intensity values of the similarity maps. This method learns a Mahalanobis distance metric to produce a featured space used to distinguish between IHC positive staining and normal tissues, as well as quantifying the reaction intensity degrees. The results obtained by the proposed method were compared to the linear classification on HSV (Hue, Saturation and Value) color space using different biomarkers categories. The comparison results show that the boundary limits of the pattern distributions are better defined in the proposed method, allowing better discrimination between normal tissues and IHC expression.