ReviewEuropean journal of nuclear medicine and molecular imaging2019
Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.
Review in European journal of nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.World journal of surgery · 2022Pooled it
- End-to-End PET/CT Interpretation and Quantification with an LLM-Orchestrated AI Agent: A Real-World Pilot Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Glioblastoma survival prediction through MRI and clinical data integration with transfer learning.International journal of computer assisted radiology and surgery · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Rapid identification ofChinese herbal medicines · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- Hypermetabolic pulmonary lesions detection and diagnosis based on PET/CT imaging and deep learning models.European journal of nuclear medicine and molecular imaging · 2025Article
- DWI-based deep learning radiomics nomogram for predicting the impaired quality of life in patients with unruptured intracranial aneurysm developing new iatrogenic cerebral infarcts following stent placement: a multicenter cohort study.Neurosurgical review · 2025Article
- Automatic bone marrow segmentation for precise [Medical physics · 2025Article
- Preliminary study on detection and diagnosis of focal liver lesions based on a deep learning model using multimodal PET/CT images.European journal of radiology open · 2025Article
- Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.European radiology · 2025Article
- Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer.Frontiers in oncology · 2025Review
- Organotropic metastasis in colorectal cancer: integrating molecular pathways with therapeutic opportunities.Frontiers in immunology · 2025Review
- Role of Radioiodine in Cancer Therapy: A Review of the Design and Challenges in Selecting Radioligands from Natural Sources.Drug design, development and therapy · 2025Review
- Carotid artery dissection recanalization: imaging modalities, influencing factors, and therapeutic perspectives.Frontiers in neurology · 2025Review
- Image Synthesis in Nuclear Medicine Imaging with Deep Learning: A Review.Sensors (Basel, Switzerland) · 2024Review
- Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?Surgical endoscopy · 2024Review
- Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.The British journal of radiology · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.
Indexed as
Identifiers
31280350What 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.