Evidence map›Paper›PMID 41744694›Full record

ArticleBiosensors2026

A Deep-Learning-Enhanced Ultrasonic Biosensing System for Artifact Suppression in Sow Pregnancy Diagnosis.

Xiaoying Wang, Jundong Wang, Ziming Gao, Xinjie Luo, Zitong Ding, Yiyang Chen, Zhe Zhang, Hao Yin, Yifan Zhang, Xuan Liang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xiaoying WangThird Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.ORCID 0009-0003-5567-2349
Jundong WangIntelligent Vehicle College, Guangzhou Polytechnic University, Guangzhou 511483, China.ORCID 0009-0003-5085-4856
Ziming GaoCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Xinjie LuoCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Zitong DingCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Yiyang ChenCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Zhe ZhangCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Hao YinCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Yifan ZhangCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Xuan LiangCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.
Qiangqiang OuyangCollege of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0001-9265-2660

Funding

Natural Science Foundation of China under grants 62203130
6 · The paper itself

Abstract

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection-masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications.

Indexed as

artifact removalartificial intelligencedeep learningsow pregnancy diagnosisultrasonic biosensorultrasound imagingYOLOv8

Identifiers

PMID41744694
PMCPMC12937970

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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.