Burning mouth syndrome (BMS) is defined as a chronic pain condition, characterized symptomatically by a generalized or localized burning sensation in the oral cavity. Various drugs have been used in attempting to treat BMS, but there is insufficient evidence to show the effect of any effective treatment. The aim of this review was to assess the effectiveness of therapies for BMS. Randomized controlled trials (RCTs) enrolling patients with a diagnosis of BMS were identified by searching Pubmed and Scoppus databases. The methodological quality of included studies was assessed on the basis of the method of allocation concealment, blindness of the study, loss of participants, size sample, and outcome concealment. A total of 12 relevant articles were analyzed. Therapies that used capsaicin, alpha-lipoic acid (ALA), and clonazepam were those that showed more reduction in symptoms of BMS. However, many studies of therapeutic interventions in BMS lack consistency in their results, because they use in their methodology, sample and a relatively short time of therapy and often do not provide a follow-up of patients treated. Thus, future studies are required to establish the treatment for patients suffering from this chronic and painful syndrome.
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.
Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.
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