Web‐based efficient dual attention networks to detect COVID‐19 from X‐ray images.

Rapid and accurate detection of COVID-19 is a crucial step to control the virus. For this purpose, the authors designed a web-based COVID-19 detector using efficient dual attention networks, called ‘EDANet’. The EDANet architecture is based on inverted residual structures to reduce the model complexity and dual attention mechanism with position and channel attention blocks to enhance the discriminant features from the different layers of the network. Although the EDANet has only 4.1 million parameters, the experimental results demonstrate that it achieves the state-of-the-art results on the COVIDx data set in terms of accuracy and sensitivity of 96 and 94%. The web application is available at the following link: https://covid19detector-cxr.herokuapp.com/

A means of assessing deep learning-based detection of ICOS protein expression in colon cancer.

Biomarkers identify patient response to therapy. The potential immune‐checkpoint bi-omarker, Inducible T‐cell COStimulator (ICOS), expressed on regulating T‐cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pa-thology, including the quantification of biomarkers. In this study, we propose a general AI‐based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user‐friendly tool that can interact with1 other open‐source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell‐based segmentation/detection to quantify and analyse the trade‐offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression

KRASFormer: a fully vision transformer-based framework for predicting KRAS gene mutations in histopathological images of colorectal cancer

Detecting the Kirsten Rat Sarcoma Virus (KRAS) gene mutation is significant for colorectal cancer (CRC) patients. The KRAS gene encodes a protein involved in the epidermal growth factor receptor (EGFR) signaling pathway, and mutations in this gene can negatively impact the use of monoclonal antibodies in anti-EGFR therapy and affect treatment decisions. Currently, commonly used methods like next-generation sequencing (NGS) identify KRAS mutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developed KRASFormer, a novel framework that predicts KRAS gene mutations from Haematoxylin and Eosin (H & E) stained WSIs that are widely available for most CRC patients. KRASFormer consists of two stages: the first stage filters out non-tumor regions and selects only tumour cells using a quality screening mechanism, and the second stage predicts the KRAS gene either wildtype’ or mutant’ using a Vision Transformer-based XCiT method. The XCiT employs cross-covariance attention to capture clinically meaningful long-range representations of textural patterns in tumour tissue and KRAS mutant cells. We evaluated the performance of the first stage using an independent CRC-5000 dataset, and the second stage included both The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) and in-house cohorts. The results of our experiments showed that the XCiT outperformed existing state-of-the-art methods, achieving AUCs for ROC curves of 0.691 and 0.653 on TCGA-CRC-DX and in-house datasets, respectively. Our findings emphasize three key consequences: the potential of using H & E-stained tissue slide images for predicting KRAS gene mutations as a cost-effective and time-efficient means for guiding treatment choice with CRC patients; the increase in performance metrics of a Transformer-based model; and the value of the collaboration between pathologists and data scientists in deriving a morphologically meaningful model

ICOSeg: real-time ICOS protein expression segmentation from immunohistochemistry slides using a lightweight conv-transformer network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters

Convolutional encoder-decoder network for road extraction from remote sensing images

In this paper, we propose a convolutional neural network, which is based on down sampling followed by up sampling architecture for the purpose of road extraction from aerial images. Our model consists of convolutional layers only. The proposed encoder-decoder structure allows our network to retain boundary information, which is a critical feature for road identification. This feature is usually lost when dealing with other CNN models. Our design is also less complex in terms of depth, number of parameters, and memory size. It, therefore, uses fewer computer resources in both training and during execution. Experimental results on Massachusetts roads dataset demonstrate that the proposed architecture, although less complex, competes with the state-of-the-art proposed approaches in terms of precision, recall, and accuracy

General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology

Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product

A Rare Case of Hypophosphataemic Osteomalacia in von Recklinghausen Neurofibromatosis

Background: Neurofibromatosis type 1 (NF1), also known as von Recklinghausen disease, is a one of the more common hereditary autosomal disorders. However, osteomalacia in neurofibromatosis type 1 is very rare tumour-induced osteomalacia; fibroblast growth factor-23 is usually implicated.Patients and methods: We report the case of a patient with a history of von Recklinghausen neurofibromatosis who presented with hypophosphataemic osteomalacia.Results: The patient was treated with high-dose calcitriol and oral phosphate with clinical improvement. Conclusion: Even though it is a rare entity, we must consider the diagnosis of hypophosphataemic osteomalacia in patients with neurofibromatosis in order to deliver appropriate treatment

Zoledronate Associated Seizure in Chronic Recurrent Multifocal Osteomyelitis

Chronic recurrent multifocal osteomyelitis (CRMO) is an auto-inflammatory disease characterized by sterile bone lesions. We report a case of a patient with CRMO who developed a seizure post bisphosphonate administration. Although, the treatment of CRMO is currently not codified, the most promising results have been observed in patients under treatment with bisphosphonates. Keywords: CRMO; Bisphosphonate; tonico-clonic seizure

Patient satisfaction with medication in rheumatoid arthritis: an unmet need

Objectives Shared decision-making between rheumatologists and patients has become an overarching principle in current treatment recommendations in rheumatoid arthritis (RA). Therefore, in the present study, we aimed to assess the satisfaction of patients with RA with their treatment and to investigate the associated factors. Material and methods A cross-sectional study was carried out in the Rheumatology Department of Mongi Slim Hospital. We included adults with RA receiving their current disease-modifying anti- rheumatic drugs for at least 12 months. Satisfaction among patients was assessed by the Treatment Satisfaction Questionnaire for Medica-tion (TSQM) and it was defined by a score ≥ 80%. The factors indirectly influencing patient satisfac-tion that were assessed were: satisfaction with medical care management, disease activity, function-al impact, professional impact, and the impact of RA. Multivariable regression analysis was applied to determine the predictors of satisfaction. Results We included 70 patients (63 female/7 male) with a mean age of 57.8 ±10.6 years. The mean disease duration was 13.71 ±7.2 years. Mean TSQM scores were 65.42 ±14.77 for convenience, 68.71 ±18 for effectiveness, 70.60 ±24.5 for side effects, and 67.95 ±17.10 for global satisfaction. Satisfaction rates were: 20% for convenience, 39% for effectiveness, 46% for side effects and 30% for global satisfaction. In multivariable analysis, the predictors of global dissatisfaction were Rheumatoid Arthritis Impact of Disease (RAID) overall score (p = 0.003) and the degree of physical difficulties (p = 0.001). Satisfac-tion with the physician was correlated with better global satisfaction (p = 0.029). Difficulties in adapt-ing to RA (p = 0.043) and current treatment with biologics (p = 0.027) were predictors of dissatisfaction with convenience. Predictors of dissatisfaction with efficiency were the RAID over-all score (p = 0.032) and the difficulties of adapting to RA (p = 0.013). The predictors of satisfaction with side effects were a lower degree of interference with domestic work (p = 0.02) and better in-volvement of the patient in the treatment decision (p = 0.014). Conclusions The satisfaction with the attending physician, the participation in the treatment decision, and the impact of RA seem to influence treatment satisfaction the most. These data suggest that a better understanding of patients’ medical needs and preferences would improve satisfaction outcomes

Validity of Remission Criteria in Rheumatoid Arthritis Compared to Ultrasound-Defined Remission

Objectives: Remission is the ultimate purpose of treatment in Rheumatoid Arthritis (RA). However, even when the most stringent composite scores were used, structural damages can occur. For that purpose ultrasonography (US) appears to be the best way to assess real remission. Our principal aim was to investigate the validity of different RA remission scores using the US as the reference. Methods: An analytic diagnostic study of 30 RA patients in remission according to DAS28 and a control group with active RA was conducted between January and October of 2018. Among them, we identified patients in remission according to the SDAI, the CDAI, and the ACR/EULAR remission score. The validity of each activity score for remission was calculated using as a gold standard the absence of PD signal. Results: All patients were in remission according to DAS28 with an average score of 2.03 [1.13-2.6]. US examination showed PD signals in 57% of all patients. Twenty-six patients were in remission according to CDAI, a Doppler signal was detected in 58% of those cases. SDAI remission was accomplished in 19 patients with PD activity in 53% of cases. For the 14 patients in remission according to ACR/EULAR criteria, synovial hyper-vascularization was found in 64%. Considering true remission as the absence of PD signals, the most sensitive and specific score was DAS28 (93% and 68% respectively). Conclusion: Considering remission in RA as the absence of vascularized synovitis, the DAS28 was the most sensitive and the most specific score. Keywords: Rheumatoid Arthritis, remission, ultrasonography, validit