AI Medical Compendium Journal:
The Journal of pathology

Showing 21 to 30 of 34 articles

Independent real-world application of a clinical-grade automated prostate cancer detection system.

The Journal of pathology
Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists...

Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.

The Journal of pathology
Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morpholo...

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

The Journal of pathology
Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection o...

Classification of intestinal T-cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status.

The Journal of pathology
In coeliac disease (CeD), immune-mediated small intestinal damage is precipitated by gluten, leading to variable symptoms and complications, occasionally including aggressive T-cell lymphoma. Diagnosis, based primarily on histopathological examinatio...

Generative models in pathology: synthesis of diagnostic quality pathology images.

The Journal of pathology
Within artificial intelligence and machine learning, a generative model is a powerful tool for learning any kind of data distribution. With the advent of deep learning and its success in image recognition, the field of deep generative models has clea...

Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI.

The Journal of pathology
The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guidelines for the design (SPIRIT) and reporting (CONSORT) of clinical trials have been extended with the a...

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

The Journal of pathology
Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to s...

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

The Journal of pathology
Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analy...

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

The Journal of pathology
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutation...

Software-assisted decision support in digital histopathology.

The Journal of pathology
Tissue diagnostics is the world of pathologists, and it is increasingly becoming digitalised to leverage the enormous potential of personalised medicine and of stratifying patients, enabling the administration of modern therapies. Therefore, the dail...