ReviewThe Journal of pathology2022
Developing image analysis methods for digital pathology.
Review in The Journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Artificial intelligence-based quantification of epidermal proliferation and apoptosis in human skin.JID innovations : skin science from molecules to population health · 2026Article
- Comparison and image analysis of different file formats on whole-slide images for colorectal and non-small cell lung carcinomas.Journal of pathology informatics · 2026Article
- Time-dependent prognostic value of automated Ki67 assessment and its integration with molecular risk profiling in WHO grade 2 meningioma.Acta neuropathologica communications · 2026Article
- Photoinduced Fluorescence Changes in Selected Bacterial Strains Under 405 nm Laser Excitation: An In Vitro Hue-Based Monitoring Approach.Micromachines · 2026Article
- Low KIF4A expression is associated with gastric cancer progression and aggressive phenotypes.Scientific reports · 2026Article
- Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression.Nature communications · 2026Article
- Variability Regularized Feature Selection (VaRFS) for optimal identification of robust and discriminable features from medical imaging.Npj imaging · 2026Article
- Overcoming Variability in RNAscope Image Analysis: A HALO-Based Approach.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Comparison of multiple quantitative strategies for neuropathologic image analyses.Journal of neuropathology and experimental neurology · 2025Article
- Article
- Novel in situ seeding immunodetection assay uncovers neuronal-driven alpha-synuclein seeding in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Digital pathology in tau research: A comparison of QuPath and HALO.Journal of neuropathology and experimental neurology · 2025Article
- Automating Colon Polyp Classification in Digital Pathology by Evaluation of a "Machine Learning as a Service" AI Model: Algorithm Development and Validation Study.JMIR formative research · 2025Article
- QuPath Edu and OpenMicroanatomy: Open-source virtual microscopy tools for medical education.Journal of anatomy · 2025Article
- Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- Article
- Neuropathological stages of neuronal, astrocytic and oligodendrocytic alpha-synuclein pathology in Parkinson's disease.Acta neuropathologica communications · 2025Article
- Use of a Pathomics Nomogram to Predict Postoperative Liver Metastasis in Patients with Stage III Colorectal Cancer.Annals of surgical oncology · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset-dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof-of-concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.