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The importance of radiology in modern medicine is acknowledged for its non-invasive diagnostic capabilities, yet the manual formulation of unstructured medical reports poses time constraints and error risks. This study addresses the common limitation of Artificial Intelligence applications in medical image captioning, which typically focus on classification problems, lacking detailed information about the patient's condition. Despite advancements in AI-generated medical reports that incorporate descriptive details from X-ray images, which are essential for comprehensive reports, the challenge persists. The proposed solution involves a multimodal model utilizing Computer Vision for image representation and Natural Language Processing for textual report generation. A notable contribution is the innovative use of the Swin Transformer as the image encoder, enabling hierarchical mapping and enhanced model perception without a surge in parameters or computational costs. The model incorporates GPT-2 as the textual decoder, integrating cross-attention layers and bilingual training with datasets in Portuguese PT-BR and English. Promising results are noted in the proposed database with ROUGE-L 0.748, METEOR 0.741, and NIH CHEST X-ray with ROUGE-L 0.404 and METEOR 0.393.

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The present work shows a computational tool developed in the MATLAB platform. Its main functionality is to evaluate a thermal model of the breast. This computational infrastructure consists of modules in which manipulate the infrared images and calculate breast temperature profiles. It also allows the analysis of breast nodules. The different modules of the framework are interconnected through an interface which the major purpose is to automatize the whole process of the infrared image analysis, in a quick and organized way. The tool is initially supplied with a three-dimensional mesh that represents the substitute geometry of the patient's breast together with her infrared images which are transformed into temperature matrices. Through these matrices, the frontal and lateral mappings are performed by specified modules. This process generates an image and a text file with all the temperatures associated to the nodes of the surface mesh. The developed tool is also able to manage the use of a commercial mesh generation program and a computational fluid dynamics code, the FLUENT, in order to validate the technique by the use of a parametric analysis. In these analyses, the tumor may have several geometric shapes and different locations within the breast.

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Human papillomavirus (HPV) infections are associated with cervical cancer and other anogenital cancers. Despite progresses in HPV vaccination and screening, these cancers still show high incidence and mortality, requiring improved prognostic markers and tailored therapies. This review addresses the role of Matrix metalloproteinases (MMPs) in HPV-induced cancers and the modulation of MMP expression by HPV oncoproteins. Scientific literature indexed in PubMed and ScienceDirect about Human papillomavirus modulates matrix metalloproteinases was retrieved and critically analyzed, to obtain an overview of expression patterns and their implications for carcinogenesis and patient prognosis. Matrix metalloproteinases such as MMP1, MMP9 and MMP13 have been associated with patient prognosis in HPV-induced cancers and play a major role in the degradation of the extracellular matrix, tumor invasion and metastasis. The HPV E2 and E7 oncoproteins regulate MMP expression via AKT, MEK/ERK and AP-1 signaling among other mechanisms. Increased expression of MMPs is associated with cancer progression and poor prognosis in multiple HPV-induced cancers, suggesting their potential use as prognostic markers. The identification of specific signaling pathways that mediate MMP regulation by HPV is essential for developing efficient new cancer therapies.

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The sixth cranial nerve, also known as the abducens nerve, is responsible for controlling the movements of the lateral rectus muscle. Palsies on the sixth nerve prevent some muscles that control eye movements from proper functioning, causing headaches, migraines, blurred vision, vertigo, and double vision. Hence, such palsy should be diagnosed in the early stages to treat it without leaving any sequela. The usual methods for diagnosing the sixth nerve palsy are invasive or depend on expensive equipment, and computer-based methods designed specifically to diagnose the aforementioned palsy were not found until the publication of this work. Therefore, a low-cost, non-invasive method can support or guide the ophthalmologist's diagnosis. In this context, this work presents a computational methodology to aid in diagnosing the sixth nerve palsy using videos to assist ophthalmologists in the diagnostic process, serving as a second opinion. The proposed method uses convolutional neural networks and image processing techniques to track both eyes' movement trajectory during the video. With this trajectory, it is possible to calculate the average velocity (AV) in which each eye moves. Since it is known that paretic eyes move slower than healthy eyes, comparing the AV of both eyes can determine if the eye is healthy or paretic. The results obtained with the proposed method showed that paretic eyes move at least 19.65% slower than healthy ones. This threshold, along with the AV of the movement of the eyes, can help ophthalmologists in their analysis. The proposed method reached 92.64% accuracy in diagnosing the sixth optic nerve palsy (SONP), with a Kappa index of 0.925, which highlights the reliability of the results and gives favorable perspectives for further clinical application.

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The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

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Strabismus is an eye disease that affects about 0.12%-9.86% of the population, which can cause irreversible sensory damage to vision and psychological problems. The most severe cases require surgical intervention, despite other less invasive techniques being available for a more conservative approach. As for surgeries, the treatment goal is to align the eyes to recover binocular vision, which demands knowledge, training, and experience. One of the leading causes of failure is human error during the measurement of deviation. Thus, this work proposes a new method based on the Decision Tree Regressor algorithms to assist in the surgical planning for horizontal strabismus to predict recoil and resection measures in the lateral and medial rectus muscles. In the presented method, two application approaches were taken, being in the form of multiple single target models, one procedure at a time, and the form of one multiple target model or all surgical procedures together. The method's efficiency is indicated by the average difference between the value indicated by the method and the physician's value. In our most accurate model, an average error of 0.66 mm was obtained for all surgical procedures, both for resection and recoil in the indication of the horizontal strabismus surgical planning. The results present the feasibility of using Decision Tree Regressor algorithms to perform the planning of strabismus surgeries, making it possible to predict correction values for surgical procedures based on medical data analysis and exceeding state-of-art.

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Age-related macular degeneration (AMD) is an eye disease that can cause visual impairment and affects the elderly over 50 years of age. AMD is characterized by the presence of drusen, which causes changes in the physiological structure of the retinal pigment epithelium (RPE) and the boundaries of the Bruch's membrane layer (BM). Optical coherence tomography is one of the main exams for the detection and monitoring of AMD, which seeks changes through the evaluation of successive sectional cuts in the search for morphological changes caused by drusen. The use of CAD (Computer-Aided Detection) systems has contributed to increasing the chances of correct detection, assisting specialists in diagnosing and monitoring disease. Thus, the objective of this work is to present a method for the segmentation of the inner limiting membrane (ILM), retinal pigment epithelium, and Bruch's membrane in OCT images of healthy and Intermediate AMD patients. The method uses two deep neural networks, U-Net and DexiNed to perform the segmentation. The results were promising, reaching an average absolute error of 0.49 pixel for ILM, 0.57 for RPE, and 0.66 for BM.

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Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Nonetheless, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to the similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients ascribed to pathological changes or different resolutions of images. In this regard, the state-of-the-art includes methods based on a probabilistic atlas, active contour models, and deep learning techniques. However, these techniques have limitations that need to be addressed, such as MRI scans with the same spatial resolution, initialization of the prostate region with well-defined contours and a set of hyperparameters of deep learning techniques determined manually, respectively. Therefore, this paper proposes an automatic and novel coarse-to-fine segmentation method for prostate 3D MRI scans. The coarse segmentation step combines local texture and spatial information using the Intrinsic Manifold Simple Linear Iterative Clustering algorithm and probabilistic atlas in a deep convolutional neural networks model jointly with the particle swarm optimization algorithm to classify prostate and non-prostate tissues. Then, the fine segmentation uses the 3D Chan-Vese active contour model to obtain the final prostate surface. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases presenting a dice similarity coefficient of 84.86%, relative volume difference of 14.53%, sensitivity of 90.73%, specificity of 99.46%, and accuracy of 99.11%. Experimental results demonstrate the high performance potential of the proposed method compared to those previously published.

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After publication, it was highlighted that the original publication [1] contained a spelling mistake in the first name of Marcelo Gattas. This was incorrectly captured as Marelo Gattass in the original article which has since been updated.

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BACKGROUND: Age-related macular degeneration (AMD) is a degenerative ocular disease that develops by the formation of drusen in the macula region leading to blindness. This condition can be detected automatically by automated image processing techniques applied in spectral domain optical coherence tomography (SD-OCT) volumes. The most common approach is the individualized analysis of each slice (B-Scan) of the SD-OCT volumes. However, it ends up losing the correlation between pixels of neighboring slices. The retina representation by topographic maps reveals the similarity of these structures with geographic relief maps, which can be represented by geostatistical descriptors. In this paper, we present a methodology based on geostatistical functions for the automatic diagnosis of AMD in SD-OCT. METHODS: The proposed methodology is based on the construction of a topographic map of the macular region. Over the topographic map, we compute geostatistical features using semivariogram and semimadogram functions as texture descriptors. The extracted descriptors are then used as input for a Support Vector Machine classifier. RESULTS: For training of the classifier and tests, a database composed of 384 OCT exams (269 volumes of eyes exhibiting AMD and 115 control volumes) with layers segmented and validated by specialists were used. The best classification model, validated with cross-validation k-fold, achieved an accuracy of 95.2% and an AUROC of 0.989. CONCLUSION: The presented methodology exclusively uses geostatistical descriptors for the diagnosis of AMD in SD-OCT images of the macular region. The results are promising and the methodology is competitive considering previous results published in literature.

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Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees. Graphical Abstract Stages of the proposed methodology.

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Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

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Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

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Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis. In addition, we use the support vector machine classifier. To test the proposed methodology, it was applied to 833 images from the LIDC-IDRI database, and cross-validation with k-fold, where [Formula: see text], was used to validate the results. The proposed methodology for the classification of nodules and non-nodules achieved a mean accuracy of 95.33 %. Lung cancer causes more deaths than any other cancer worldwide. Therefore, precocious detection allows for faster therapeutic intervention and a more favorable prognosis for the patient. Our proposed methodology contributes to the classification of lung nodules and should help in the diagnosis of lung cancer.

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Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.

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OBJETIVO: propor um método para segmentação de microcalcificações em imagens mamográficas por meio do algoritmo firefly. MATERIAIS E MÉTODO: aplicar as etapas de aquisição das imagens, pré-processamento e segmentação. RESULTADOS: foram obtidos para as imagens densas 91% de acerto e para as imagens não densas 95% de acerto na detecção das microcalcificações. CONCLUSÃO: o método mostrou-se viável como instrumento para auxílio na detecção de microcalcificações em imagens mamográficas densas e não densas.

OBJECTIVE: proposing a method for microcalcifications segmentation in mammographic images by means of firefly algorithm. MATERIALS AND METHODS: apply the steps of acquisition, preprocessing and segmentation. RESULTS: the dense images resulted 91% of accuracy and non-dense images 95% of accuracy in the detection of microcalcifications. CONCLUSION: The method proved to be feasible as a tool to aid in the detection of microcalcifications in both dense and non-dense mammographic images.

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Uma forma de verificar a malignidade de lesões em mamografias é o acompanhamento periódico, analisando mudanças em medições de geometria (forma) e textura (tecido). Uma das medidas de forma mais utilizadas é a taxa de crescimento. No entanto, somada a medidas de tecido, obtém-se informações úteis sobre o desenvolvimento interno da lesão. OBJETIVOS: Uma metodologia para estabelecer uma correspondência entre lesões em mamografias de tempos diferentes e analisar as mudanças no tecido através de índices de similaridade. MÉTODOS: Executado em cinco etapas: Aquisição das Imagens, Pré-processamento, Registro de Imagens, Correspondência entre as Lesões e Análise Temporal de Texturas. RESULTADOS: Os resultados preliminares mostram que essa metodologia é promissora na detecção de mudanças no tecido das lesões. CONCLUSÃO: Os índices de similaridade se mostraram eficientes na quantificação de mudanças na textura e podem ser usados como informações para auxiliar o acompanhamento e diagnóstico de doenças associadas as lesões.

One way to verify the malignancy of breast lesions is the temporal analysis measurement geometry (shape) and texture (tissue). In this sense, one of the most used form measures is the growth rate. However, in addition to tissue measurements over time, you get useful information about their behavior. OBJECTIVES: A methodology for establishing a correspondence between injuries at different times and analyze changes in tissue through similarity indices. METHODS: Executed in five steps: image acquisition, preprocessing, Image Registration, Correspondence between Lesions and Temporal Analysis of Lesions Texture. RESULTS: Preliminary results show that this method is promising for detecting changes in tissue lesions. CONCLUSION: The similarity indices were effective in quantitating changes in texture and can be used as information to assist the monitoring and diagnosis of lesions associated diseases.

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OBJETIVO: predizer o estado volumétrico de lesões pulmonares aplicando o modelo oculto de Markov (HMM). MATERIAIS E MÉTODOS: Aquisição de imagens de lesões pulmonares temporais, geração do HMM e a aplicação do HMM. RESULTADOS: Os testes foram aplicados em 24 lesões pulmonares, adquiridas da Public Lung Database to Address Drug Response (PLDADR). Dividimos os resultados desta pesquisa em 3. O primeiro utilizando a base completa para predição volumétrica da lesão e comparação com o Response Evaluation Criteria in Solid Tumors (RECIST), atingindo uma taxa de acerto de 70,83%. No segundo, Aplica - se o método leave-one-out, separando os dados em dois grupos, treino e teste, obtendo-se uma taxa de acerto de 75,00%. Por fim, realizamos a predição volumétrica de cada lesão no intervalo de 5 tempos. O resultado mostrou que é possível predizer se o estado da lesão está progredindo, regredindo ou estabilizando, a partir das alterações ocorridas nos diâmetros e volumes.

OBJECTIVE: predicting the volume status of lung lesions by applying the hidden Markov model (HMM). MATERIALS AND METHODS: Acquisition of images of temporal lung lesions, HMM generation and application of HMM. RESULTS: The tests were applied in 24 pulmonary lesions, acquired from Public Lung Database to Address Drug Response(PLDADR). We have divided this search in 3. The first using the full volumetric basis for prediction of the lesion and compared to the Response Evaluation Criteria in Solid Tumors (RECIST), reaching a 70.83% success rate. Then, weapply the leave-one-out method, separating the data into two groups, training and testing, yielding a 75.00% successrate. Finally, we volumetric prediction of each lesion in 5 days interval. The result showed that it is possible to predict the state of the injury is progressing, regressing or stabilizing, from changes in the diameters and volumes.

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O glaucoma é uma das doenças que mais causam cegueira em todo o mundo. O Conselho Brasileiro de Oftalmologia (CBO) estima que no Brasil existam 985 mil portadores de glaucoma com mais de 40 anos de idade. A utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de detecção e diagnósticos corretos,auxiliando os especialistas na tomada de decisões sobre o tratamento do glaucoma. OBJETIVO: Apresentar um método para diagnóstico do glaucoma em retinografias utilizando o LBP para representar a região do disco ótico, funções geoestatísticas para descrever padrões e o MVS para classificar as imagens. MÉTODOS: Executado em 3 etapas: Representação da imagem (1), Extração de Características com geoestatística (2) e Classificação e Validação (3). RESULTADOS: Foram obtidos 88% de especificidade, 82% de sensibilidade e 84% de acurácia no diagnóstico do glaucoma. CONCLUSÃO: O método mostrou-se promissor como uma forma de auxílio ao diagnóstico de glaucoma.

Glaucoma is one of the diseases that more cause blindness worldwide. The Brazilian Council of Ophthalmology (CBO) estimates that in Brazil there are 985,000 people with glaucoma over 40 years old. The use of CAD and CADxsystems has contributed to increase the chances of detection and correct diagnoses, they provide, helping specialists inmaking decisions on glaucoma treatment. OBJECTIVE: To introduce a method for diagnosing glaucoma in fundus imageusing the LBP to represent the optic disk region, geostatistical functions to describe patterns and SVM to classify the images. METHODS: Run in 3 steps: Image representation (2), Feature extraction with geostatistic (3) and Classification and Validation (4). RESULTS: we obtained 88% specificity, 82% sensitivity and 84% accuracy in the diagnosis of glaucoma. CONCLUSION: The method has shown promise as a tool to aid the diagnosis of glaucoma.

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As tecnologias de Realidade Virtual vêm se desenvolvendo bastante nos últimos anos e com elas a sua utilização em diversas áreas, dentre as quais, a medicina. Testes, treinamentos, e alguns tipos de tratamento que seriam complicados de serem ser feitos com abordagens tradicionais agora podem ser produzidos graças aos elementos disponíveis nas tecnologias de realidade virtual. OBJETIVO: Propor uma ferramenta de visualização volumétrica em realidade virtual que possua interação gestual e ferramentas de segmentação de imagens e que facilite o processo de análise de dados médicos. MÉTODOS: Aquisição das imagens, geração dos dados volumétricos, desenvolvimento das ferramentas de interação e desenvolvimento da interface gestual. RESULTADOS: O sistema obteve êxito na geração e visualização de dados médicos tendo bom desempenho em testes realizados na avaliação de usabilidade de sua interface gestual. CONCLUSÃO: O sistemas e mostra como uma promissora alternativa para a visualização de dados médicos em realidade virtual.

The Virtual Reality technologies have been developing greatly in recent years and with them their use in various fields, among which the medicine. Some testings, trainings, and some types of treatments that would be complicated to be made with traditional approaches can now be produced thanks to the elements available in the virtual reality technologies. OBJECTIVE: To propose a volume visualization tool in virtual reality that has gestural interaction and image segmentation tools and facilitates the process of analysis of medical data. METHODS: Image acquisition volumetric data generation, development of the interaction tools and development of the gestural interface. RESULTS: The system was successful in the generation and visualization of medical data, having good performance in usability tests of its gestural interface. CONCLUSION: The system is a promising alternative for viewing medical data in virtual reality.

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