ClinicalTrials.gov
public full text
Public Domain
Evaluation and Treatment Strategy Development of Coronary Heart Disease Guided by OCT Based on Multimodal Deep Learning
This is a retrospective, multicenter, observational study aimed at assessing stent apposition for coronary stent implantation by an optical coherence tomography system constructed by deep learning algorithms and evaluating the prognosis of patients after stent implantation in conjunction with multimodal diagnostic and therapeutic information.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multi-stage deep learning architecture for carotid artery segmentation and stenosis evaluation: comparative study with digital subtraction angiography.#170626
- Detection of Valvular Heart Disease Using Artificial Intelligence-based Stethoscope#912642
- Comparative evaluation of deep generative models for predicting 12-month neovascular AMD progression using OCT and fundus photography#679754
- Evaluating Deep-Learning Based Quantification of Breast Arterial Calcification on Mammography for Cardiovascular Risk Assessment#377131
- Collecting Respiratory Sound Samples From Corona Patients to Extend the Diagnostic Capability of VOQX Electronic Stethoscope to Diagnose COVID-19 Patients#833934
Record · ID 837264
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive