ArticleAssay and drug development technologies2020
Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.
Article in Assay and drug development technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy.International ophthalmology · 2026Article
- High performance deep-learning model for the diagnosis of auto-immune hepatitis based on histological whole slide images.Virchows Archiv : an international journal of pathology · 2026Article
- Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative/neoplastic regions in a thioacetamide-induced rat liver model.Frontiers in artificial intelligence · 2026Article
- Automated machine learning predicts liver metastases in patients with early-onset gastroenteropancreatic neuroendocrine tumors.Journal of gastrointestinal oncology · 2025Article
- Liver Sinusoidal Endothelial Cells in the Regulation of Immune Responses and Fibrosis in Metabolic Dysfunction-Associated Fatty Liver Disease.International journal of molecular sciences · 2025Review
- BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.American journal of physiology. Gastrointestinal and liver physiology · 2024Article
- Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.Scientific reports · 2024Article
- Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.Journal of personalized medicine · 2024Review
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury.Scientific reports · 2023Article
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automated machine learning to predict the difficulty for endoscopic resection of gastric gastrointestinal stromal tumor.Frontiers in oncology · 2023Article
- Review
- The Promise of AI for DILI Prediction.Frontiers in artificial intelligence · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
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.