ArticleScientific reports2026
A lightweight GhostYOLO framework with GhostDynamic Module and multi-scale attention for thyroid nodule diagnosis.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Abstract
Ultrasound is the primary tool for thyroid nodule assessment, but diagnostic accuracy and efficiency require improvement. This study aims to develop a high-performance, computationally efficient deep learning model for thyroid nodule detection. We propose GhostYOLO, a novel framework based on YOLOv11. It incorporates a GhostDynamic Module to reduce complexity and a Multi-Scale Attention (MSAttention) mechanism to improve feature extraction across varying nodule sizes. The model was trained and evaluated on a large internal dataset of 12,385 ultrasound images from 3,140 patients and validated on an external public dataset of 8,500 images from 842 cases. On the internal test set, GhostYOLO achieved a mean Average Precision (mAP) of 67.2% and an F1-Score of 86.3%. External validation yielded a mAP50 of 74.4% (mAP: 48.9%) and an F1-Score of 79.5%. The model demonstrated high efficiency with only 9.3 million parameters and 20.2 GFLOPs. GhostYOLO offers a robust and computationally efficient solution for automated thyroid nodule detection, showing strong potential to aid in clinical diagnosis.
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.