Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

Decoding pathology: the role of computational pathology in research and diagnostics

Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.

New approaches to acute kidney injury

Acute kidney injury (AKI) is a common and serious clinical syndrome that involves complex interplay between different cellular, molecular, metabolic and immunologic mechanisms. Elucidating these pathophysiologic mechanisms is crucial to identify novel biomarkers and therapies. Recent innovative methodologies and the advancement of existing technologies has accelerated our understanding of AKI and led to unexpected new therapeutic candidates. The aim of this review is to introduce and update the reader about recent developments applying novel technologies in omics, imaging, nanomedicine and artificial intelligence to AKI research, plus to provide examples where this can be translated to improve patient care.

Spatial transcriptomics in health and disease

The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

Deep learning-based classification of the capillary ultrastructure in human skeletal muscles

Background

Capillary ultrastructure in human skeletal muscles is dynamic and prone to alterations in response to many stimuli, e.g., systemic pathologies such as diabetes mellitus and arterial hypertension. Using transmission electron microscopy (TEM) images, several studies have been conducted to quantify the capillary ultrastructure by means of morphometry. Deep learning techniques like convolutional neural networks (CNNs) are utilized to extract data-driven characteristics and to recognize patterns. Hence, the aim of this study was to train a CNN to identify morphometric patterns that differ between capillaries in muscle biopsies of healthy participants and patients with systemic pathologies for the purpose of hypothesis generation.

Methods

In this retrospective study we used 1810 electron micrographs from human skeletal muscle capillaries derived from 70 study participants which were classified as "healthy" controls or "patients" in dependence of the absence or presence of a documented history of diabetes mellitus, arterial hypertension or peripheral arterial disease. Using these micrographs, a pre-trained open-access CNN (ResNet101) was trained to discriminate between micrographs of capillaries of the two groups. The CNN with the highest diagnostic accuracies during training were subsequently compared with manual quantitative analysis of the capillary ultrastructure to distinguish between "healthy" controls and patients.

Results

Using classification into controls or patients as allocation reference, receiver-operating-characteristics (ROC)-analysis of manually obtained BM thickness showed the best diagnostic accuracy of all morphometric indicators (area under the ROC-curve (AUC): 0.657 ± 0.050). The best performing CNN demonstrated a diagnostic accuracy of 79% (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed a significant difference (p < 0.001) between the AUC of the best performing CNN and the BM thickness. The underlying morphology responsible for the network prediction focuses mainly on debridement of pericytes.

Conclusion

The hypothesis-generating approach using pretrained CNN distinguishes between capillaries depicted on electron micrographs of "healthy" controls and participants with a systemic pathology more accurately than by commonly used morphometric analysis.

Deep learning applications for kidney histology analysis

PURPOSE OF REVIEW

Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pathology practices. Here, we discuss basic concepts of deep learning, recent applications in nephropathology, current challenges in implementation and future perspectives.

RECENT FINDINGS

Deep learning models have been developed and tested in various areas of nephropathology, for example, predicting kidney disease progression or diagnosing diseases based on imaging and clinical data. Despite their promising potential, challenges remain that hinder a wider adoption, for example, the lack of prospective evidence and testing in real-world scenarios.

SUMMARY

Deep learning offers great opportunities to improve quantitative and qualitative kidney histology analysis for research and clinical nephropathology diagnostics. Although exciting approaches already exist, the potential of deep learning in nephropathology is only at its beginning and we can expect much more to come.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.