LNDb v4: pulmonary nodule annotation from medical reports.

Given the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians' analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)-a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types.

Diagnostic Performance of Deep Learning Models for Gastric Intestinal Metaplasia Detection in Narrow-band Images.

Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable inter-fold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.Clinical relevance- Enhanching a clinician's ability to detect and localize intestinal metaplasia can be a crucial tool for gastric cancer management policies.

Medical Challenges of a Common Variable Immunodeficiency With a TNFRSF13B Gene Mutation in a Simultaneous Kidney and Pancreas Transplant Recipient.

Common variable immune deficiency (CVID) is a primary immunodeficiency disorder, with hypogammaglobulinemia and increased susceptibility to recurrent infections, autoimmune disorders, granulomatous diseases and malignancy. Among the solid organ transplant (SOT) recipient population, those with primary immunodeficiency disorders under chronic immunosuppression therapy can theoretically be at higher risk of atypical infections, autoimmune complications and disease recurrence with suboptimal long term graft survival, but literature is scarce. Here, we report a 27-year-old female with type 1 diabetes mellitus, complicated with nephropathy that progressed to end-stage renal disease (ESRD), who had a history of a chronic inflammatory response dysregulation, with chronic monoarthritis, persistent elevation of inflammation markers, recurrent infections, low immunoglobulin G (IgG) and A (IgA) serum levels, a slightly decreased population of memory B cells at flow cytometric immunophenotyping, and a confirmed pathological heterozygous mutation in the tumor necrosis factor receptor superfamily 13B (TNFRSF13B), with a suspected diagnosis of CVID. Whilst on hemodialysis, she received a simultaneous kidney and pancreas transplant from a standard criteria donor (SCD), and our induction and maintenance immunosuppression protocol and prophylaxis regimen allowed for a successful transplant with immediate pancreatic function, with no evidence of renal graft rejection upon biopsy in the early post-transplant period, and no novel episodes of serious infectious complications were recorded during a follow-up period of six months.

D313Y variant in two related end-stage renal disease patients - Pathogenic or not yet?

Fabry disease is a multisystem lysosomal storage disorder caused by mutations in the GLA gene that result in a deficient or absent activity of alpha-galactosidase A. There is a wide spectrum of GLA gene variants, some of which are described as non-pathogenic. The clinical importance of the D313Y variant is still under debate, although in recent years it has been considered as a variant of unknown significance or a benign variant. Despite this prevailing notion, there are multiple case reports of patients with D313Y variant that presented signs and symptoms consistent with FD without any other etiological explanation. In this article, we present two family members with an important renal phenotype and other typical manifestations of FD (white matter lesions and left ventricular hypertrophy) that only had the D313Y variant. These cases suggest that this variant of unknown significance may contribute to the development of common features of FD and should not be undervalued.

Supervised and semi-supervised training of deep convolutional neural networks for gastric landmark detection.

This work focuses on detection of upper gas-trointestinal (GI) landmarks, which are important anatomical areas of the upper GI tract digestive system that should be photodocumented during endoscopy to guarantee a complete examination. The aim of this work consisted in testing new automatic algorithms, specifically based on convolutional neural network (CNN) systems, able to detect upper GI landmarks, that can help to avoid the presence of blind spots during esophagogastroduodenoscopy. We tested pre-trained CNN architectures, such as the ResNet-50 and VGG-16, in conjunction with different training approaches, including the use of class weights, batch normalization, dropout, and data augmentation. The ResNet-50 model trained with class weights was the best performing CNN, achieving an accuracy of 71.79% and a Mathews Correlation Coefficient (MCC) of 65.06%. The combination of supervised and unsupervised learning was also explored to increase classification performance. In particular, convolutional autoencoder architectures trained with unlabeled GI images were used to extract representative features. Such features were then concatenated with those extracted by the pre-trained ResNet-50 architecture. This approach achieved a classification accuracy of 72.45% and an MCC of 65.08%. Clinical relevance- Esophagogastroduodenoscopy (EGD) photodocumentation is essential to guarantee that all areas of the upper GI system are examined avoiding blind spots. This work has the objective to help the EGD photodocumentation monitorization by testing new CNN-based systems able to detect EGD landmarks.

Analysis of classification tradeoff in deep learning for gastric cancer detection.

This study aimed to build convolutional neural network (CNN) models capable of classifying upper endoscopy images, to determine the stage of infection in the development of a gastric cancer. Two different problems were covered. A first one with a smaller number of categorical classes and a lower degree of detail. A second one, consisting of a larger number of classes, corresponding to each stage of precancerous conditions in the Correa's cascade. Three public datasets were used to build the dataset that served as input for the classification tasks. The CNN models built for this study are capable of identifying the stage of precancerous conditions/lesions in the moment of an upper endoscopy. A model based on the DenseNet169 architecture achieved an average accuracy of 0.72 in discriminating among the different stages of infection. The trade-off between detail in the definition of lesion classes and classification performance has been explored. Results from the application of Grad CAMs to the trained models show that the proposed CNN architectures base their classification output on the extraction of physiologically relevant image features. Clinical relevance- This research could improve the accuracy of upper endoscopy exams, which have margin for improvement, by assisting doctors when analysing the lesions seen in patient's images.

Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice.

Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

Lupus-like nephritis with positive anti-neutrophil cytoplasmic antibodies and negative antinuclear antibodies / Nefrite semelhante à lúpica com anticorpos citoplasmáticos antineutrófilos positivos e anticorpos antinucleares negativos

Abstract Antineutrophil cytoplasmic antibodies (ANCAs) are associated with small vessel vasculitis but their prevalence is not rare in other immune diseases. In lupus nephritis (LN), their pathological role and clinical relevance have been the target of controversial views. We present a case of acute kidney injury and nephrotic syndrome in a young woman with diffuse global proliferative and membranous nephritis on her kidney biopsy, showing a full-house immunofluorescence pattern, very allusive of class IV + V LN, but lacking associated clinical criteria and laboratory findings to support the diagnosis of systemic lupus erythematosus (SLE). Furthermore, the patient presented with high titers of ANCA, steadily decreasing alongside the renal function and proteinuria improvements, with mycophenolate mofetil (MMF) and steroid treatment. The authors believe this is a case of lupus-like nephritis, in which ANCAs are immunological markers, although they are not directly involved in the pathogenesis.

Resumo Os anticorpos anticitoplasma de neutrófilos (ANCAs) estão associados à vasculite de pequenos vasos, no entanto, a sua prevalência não é rara em outras doenças imunológicas. Na nefrite lúpica (LN), o seu papel patológico e relevância clínica têm sido alvo de pontos de vista controversos. Apresentamos um caso de lesão renal aguda e síndrome nefrótica em uma jovem com nefrite proliferativa difusa e membranosa em sua biópsia renal, muito alusivo a NL classe IV + V, com um padrão full house na imunofluorescência, mas sem critérios clínicos e achados laboratoriais para corroborar o diagnóstico de lúpus eritematoso sistêmico (LES). Não obstante, a paciente apresentou títulos elevados de ANCA, que diminuiram progressivamente com a melhoria da função renal e da proteinúria, após tratamento com micofenolato de mofetil (MMF) e esteróide. Os autores acreditam que se trata de um caso de nefrite semelhante à nefrite lúpica, em que os ANCAs são marcadores imunológicos, embora não estejam diretamente envolvidos na patogênese.

Lupus-like nephritis with positive anti-neutrophil cytoplasmic antibodies and negative antinuclear antibodies.

Antineutrophil cytoplasmic antibodies (ANCAs) are associated with small vessel vasculitis but their prevalence is not rare in other immune diseases. In lupus nephritis (LN), their pathological role and clinical relevance have been the target of controversial views. We present a case of acute kidney injury and nephrotic syndrome in a young woman with diffuse global proliferative and membranous nephritis on her kidney biopsy, showing a full-house immunofluorescence pattern, very allusive of class IV + V LN, but lacking associated clinical criteria and laboratory findings to support the diagnosis of systemic lupus erythematosus (SLE). Furthermore, the patient presented with high titers of ANCA, steadily decreasing alongside the renal function and proteinuria improvements, with mycophenolate mofetil (MMF) and steroid treatment. The authors believe this is a case of lupus-like nephritis, in which ANCAs are immunological markers, although they are not directly involved in the pathogenesis.

Deep Convolutional Neural Network Ensembles For Multi-Classification of Skin Lesions From Dermoscopic and Clinical Images.

In this paper, we consider the problem of classifying skin lesions into multiple classes using both dermoscopic and clinical images. Different convolutional neural network architectures are considered for this task and a novel ensemble scheme is proposed, which makes use of a progressive transfer learning strategy. The proposed approach is tested over a dataset of 4000 images containing both dermoscopic and clinical examples and it is shown to achieve an average specificity of 93.3% and an average sensitivity of 79.9% in discriminating skin lesions belonging to four different classes.

Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis.

OBJECTIVE: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. DESIGN: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. RESULTS: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. CONCLUSION: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice.

Active Contours Based Segmentation and Lesion Periphery Analysis For Characterization of Skin Lesions in Dermoscopy Images.

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.

Content-Adaptive Region-Based Color Texture Descriptors for Medical Images.

The design of computer-assisted decision (CAD) systems for different biomedical imaging scenarios is a challenging task in computer vision. Sometimes, this challenge can be attributed to the image acquisition mechanisms since the lack of control on the cameras can create different visualizations of the same imaging site under different rotation, scaling, and illumination parameters, with a requirement to get a consistent diagnosis by the CAD systems. Moreover, the images acquired from different sites have specific colors, making the use of standard color spaces highly redundant. In this paper, we propose to tackle these issues by introducing novel region-based texture, and color descriptors. The proposed texture features are based on the usage of analytic Gabor filters (for compensation of illumination variations) followed by the calculation of first- and second-order statistics of the filter responses and making them invariant using some trivial mathematical operators. The proposed color features are obtained by compensating for the illumination variations in the images using homomorphic filtering followed by a bag-of-words approach to obtain the most typical colors in the images. The proposed features are used for the identification of cancer in images from two distinct imaging modalities, i.e., gastroenterology and dermoscopy . Experiments demonstrate that the proposed descriptors compares favorably to several other state-of-the-art methods, elucidating on the effectiveness of adapted features for image characterization.

A multicenter prospective study of the real-time use of narrow-band imaging in the diagnosis of premalignant gastric conditions and lesions.

BACKGROUND AND AIM: Some studies suggest that narrow-band imaging (NBI) can be more accurate at diagnosing gastric intestinal metaplasia and dysplasia than white-light endoscopy (WLE) alone. We aimed to assess the real-time diagnostic validity of high resolution endoscopy with and without NBI in the diagnosis of gastric premalignant conditions and to derive a classification for endoscopic grading of gastric intestinal metaplasia (EGGIM). METHODS: A multicenter prospective study (five centers: Portugal, Italy, Romania, UK, USA) was performed involving the systematic use of high resolution gastroscopes with image registry with and without NBI in a centralized informatics platform (available online). All users used the same NBI classification. Histologic result was considered the diagnostic gold standard. RESULTS: A total of 238 patients and 1123 endoscopic biopsies were included. NBI globally increased diagnostic accuracy by 11 percentage points (NBI 94 % vs. WLE 83 %; P < 0.001) with no difference in the identification of Helicobacter pylori gastritis (73 % vs. 74 %). NBI increased sensitivity for the diagnosis of intestinal metaplasia significantly (87 % vs. 53 %; P < 0.001) and for the diagnosis of dysplasia (92 % vs. 74 %). The added benefit of NBI in terms of diagnostic accuracy was greater in OLGIM III/IV than in OLGIM I/II (25 percentage points vs. 15 percentage points, respectively; P < 0.001). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for EGGIM in the identification of extensive metaplasia was 0.98. CONCLUSIONS: In a real-time scenario, NBI demonstrates a high concordance with gastric histology, superior to WLE. Diagnostic accuracy higher than 90 % suggests that routine use of NBI allows targeted instead of random biopsy samples. EGGIM also permits immediate grading of intestinal metaplasia without biopsies and merits further investigation.

Tracking of the anterior mitral leaflet in echocardiographic sequences using active contours.

Echocardiography assessment of cardiac valves plays a vital role in the diagnosis of rheumatic heart disease. In the vast majority of cases, the mitral valve gets affected, leading to the thickening of its leaflets that may result in the fusion of their tips. This changes the appearance and reduces the mobility of the leaflets, which also reduce the heart efficiency. Quantifying such parameters provides diagnostic insight. To achieve that, the first step is to identify and then track fast moving leaflets. This work is focused on Anterior Mitral Leaflet (AML) tracking. Open ended active contours are employed in this work by removing its boundary conditions. The external and internal energy of the contour is modified that extend the capture range, improve snake energy and encourages the leftmost end point of the contour to converge on the moving tip of the AML. Results show that contour points are tracked accurately with an average error of 4.9 pixels and a standard deviation of 2.1 pixels in 9 fully annotated normal sequences of real children clinical assessments.

Identifying technology interaction opportunities within a gastroenterology exam room.

Gastric cancer is a serious disease that most people usually do not know they have until they start to get symptoms. Gastroenterology imaging is an essential tool for this battle, since an early diagnosis typically leads to a good prognosis. However, this is a rapidly evolving technological area with novel imaging devices such as capsule, narrow-band imaging or high-definition endoscopy. Adapting to these technologies has a high time-price cost, even for experienced clinicians, motivating the appearance of interactive environments that can accelerate these training processes. The GEMINI (Gastroenterology Made Interactive) project aims to create an interactive clinical decision support system (CDSS) that can be used to help with the diagnosis within a gastroenterology room during real endoscopic examinations. We used human computer interaction (HCI) support methodologies in order to identify interaction opportunities. As a final conclusion, the most promising avenue for interactions with CDSS is probably using mobile devices such as tablets, controlled by a nurse at the physician's request. As future work, we will prototype and evaluate such a system in a real hospital environment.

Detection and separation of overlapping cells based on contour concavity for Leishmania images.

Life scientists often must count cells in microscopy images, which is a tedious and time-consuming task. Automatic approaches present a solution to this problem. Several procedures have been devised for this task, but the majority suffer from performance degradation in the case of cell overlap. In this article, we propose a method to determine the positions of macrophages and parasites in fluorescence images of Leishmania-infected macrophages. The proposed strategy is primarily based on blob detection, clustering, and separation using concave regions of the cells' contours. In comparison with the approaches of Nogueira (Master's thesis, Department of University of Porto Computer Science, 2011) and Leal et al. (Proceedings of the 9th international conference on Image Analysis and Recognition, Vol. II, ICIAR'12. Berlin, Heidelberg: Springer-Verlag; 2012. pp. 432-439), which also addressed this type of image, we conclude that the proposed methodology achieves better performance in the automatic annotation of Leishmania infections.

Detecting melanoma in dermoscopy images using scale adaptive local binary patterns.

Recent advances in the area of computer vision has led to the development of various assisted diagnostics systems for the detection of melanoma in the patients. Texture and color are considered as two fundamental visual characteristics which are vital for the detection of melanoma. This paper proposes the use of a combination of texture and color features for the classification of dermoscopy images. The texture features consist of a variation of local binary pattern (LBP) in which the strength of the LBPs is used to extract scale adaptive patterns at each pixel, followed by the construction of a histogram. For color feature extraction, we used standard HSV histograms. The extracted features are concatenated to form a feature vector for an image, followed by classification using support vector machines. Experiments show that the proposed feature set exhibits good classification performance comparing favorably to other state-of-the-art alternatives.

Local self similar descriptors: comparison and application to gastroenterology images.

Local descriptors coupled with robust methods for learning visual dictionaries have been a pivotal tool in computer vision. Although the identification of similar patterns is commonly conducted on some stage of the bag-of-words framework, a prior assessment of spatial local similarities can be indicative of specific objects, and thus improved recognition rates. In this work we delve a function of similarity for enhancing the discriminative power of local constrained SIFT descriptors. Motivated by gastrointestinal images where diagnosis through endoscopy plays a decisive role in cancer detection and resulting prognosis, visual cues in these early stages are slim and of difficult perception. In order to capture these patterns we propose a self-similarity approach (based on a neighbourhood analysis of SIFT descriptors) to assess local variances through a weight function. Based on extensive simulations our approach achieved a performance of 88%: 3% higher than the standard SIFT, 10% higher than Haar wavelet and 13% higher than LBPs.

Integral scale histogram local binary patterns for classification of narrow-band gastroenterology images.

The introduction of various novel imaging technologies such as narrow-band imaging have posed novel image processing challenges to the design of computer assisted decision systems. In this paper, we propose an image descriptor referred to as integrated scale histogram local binary patterns. We propagate an aggregated histogram of local binary patterns of an image at various resolutions. This results in low dimensional feature vectors for the images while incorporating their multiresolution analysis. The descriptor was used to classify gastroenterology images into four distinct groups. Results produced by the proposed descriptor exhibit around 92% accuracy for classification of gastroenteroloy images outperforming other state-of-the-art methods, endorsing the effectiveness of the proposed descriptor.