ReviewDiagnostics (Basel, Switzerland)2023
Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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
17 citing papers in PubMed, 56 citations in OpenAlex.
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Hybrid Decision-Level Fusion of CNN-Based Deep and Handcrafted Features for Colon Cancer Classification.Journal of imaging · 2026Article
- Morphometric and Gray-Level Texture Analyses of Postmortem Injuries Using ImageJ: An Exploratory Study.Cureus · 2026Article
- Staining normalization in histopathology: Method benchmarking using multicenter dataset.Scientific reports · 2026Article
- Histopathology of Diabetic Nephropathy: Beyond Glomerular Basement Membrane Thickening.Cureus · 2025Review
- Fine-Tuning Models for Histopathological Classification of Colorectal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors.Scientific reports · 2025Article
- The Prognostic Impact of the Tumor Immune Microenvironment in Synovial Sarcoma: An Immunohistochemical Analysis Using Digital Pathology and Conventional Interpretation.Journal of personalized medicine · 2025Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine.Frontiers in oncology · 2025Article
- Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes.Journal of pathology informatics · 2025Article
- Innovative approach for the qualitative-quantitative assessment of neurodevelopment biomarkers research in placenta tissue using immunohistochemistry digital image analysis.Biology methods & protocols · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 2025Review
- Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.Computational and structural biotechnology journal · 2024Article
- Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.Pediatric surgery international · 2023Article
- Unexpected Dramatic Evolution of Placenta Increta: Case Report and Literature Review.Journal of personalized medicine · 2023Article
- Developments in Image Processing Using Deep Learning and Reinforcement Learning.Journal of imaging · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern clinical practice, digital pathology has an essential role, being a technological necessity for the activity in the pathological anatomy laboratories. The development of information technology has majorly facilitated the management of digital images and their sharing for clinical use; the methods to analyze digital histopathological images, based on artificial intelligence techniques and specific models, quantify the required information with significantly higher consistency and precision compared to that provided by optical microscopy. In parallel, the unprecedented advances in machine learning facilitate, through the synergy of artificial intelligence and digital pathology, the possibility of diagnosis based on image analysis, previously limited only to certain specialties. Therefore, the integration of digital images into the study of pathology, combined with advanced algorithms and computer-assisted diagnostic techniques, extends the boundaries of the pathologist's vision beyond the microscopic image and allows the specialist to use and integrate his knowledge and experience adequately. We conducted a search in PubMed on the topic of digital pathology and its applications, to quantify the current state of knowledge. We found that computer-aided image analysis has a superior potential to identify, extract and quantify features in more detail compared to the human pathologist's evaluating possibilities; it performs tasks that exceed its manual capacity, and can produce new diagnostic algorithms and prediction models applicable in translational research that are able to identify new characteristics of diseases based on changes at the cellular and molecular level.
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What OpenQuestion holds
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 OpenQuestion graph.