Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning

Background: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma. Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection, Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance.Comment: 26 pages, 5 figures, 8 tables + Supplementary material

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance

Development of techniques to determine extracellular matrix alterations in acute and chronic lung diseases and bioengineered tissues

Acute and chronic lung diseases are a major cause of global mortality. While pharmacological approaches exist, no therapies are curative. The only option at end-stage disease is lung transplantation which is hampered by a chronic shortage of donor organs. Therefore, there is a high interest to develop alternative approaches to use regenerative medicine approaches to generate new lung tissue in the lab or to deliver cells which can participate in structural repair. In parallel to this clinical need, these new technologies, and the animal models which are used to assess their efficacy, require the development of new evaluation methods. One of the most important methods for evaluating these therapies is histological assessment, as it can provide direct information at the tissue and cellular level information across all stages of the bioengineered tissue: from manufacture through evaluation in pre-clinical animal models. However, many of these potential therapies are comprised of a mix of cells, extracellular matrix and biomaterials (i.e. polymers in the case of soft tissues). Standard histological approaches have been developed for use with native animal and human tissues and organs, based on chemical moieties which are ubiquitous in animal tissues (e.g. amine or carboxylic acid groups). Biomaterials (e.g. synthetic or natural polymers), on the other hand, have diverse chemical moieties that may not always be compatible with standard fixatives and tissue processing. Furthermore, the chemical composition of the solutions used in fixation or tissue processing, even at trace amounts, may alter biomaterials which have been used for bioengineering tissue or in vitro models. Therefore, this thesis aimed to develop new methods to histologically assess native and bioengineered lung tissue, with a particular focus on developing methods which preserve cell-extracellular matrix or cell-biomaterial interactions for light and electron-based microscopy