{
  "abstract": "Kidney stones are a common and serious urinary tract disease. Due to their small size, indistinct boundaries, and differences in imaging protocols and scanners across centers, deep learning-based image-level classification on non-contrast computed tomography (NCCT) images still faces challenges in reliably distinguishing stone-positive images from normal images and achieving robust cross-center performance. To this end, we developed a deep learning-based image-level NCCT kidney stone classification framework that encompasses dataset construction, unified preprocessing, systematic comparison of image enhancement strategies, modeling of a multi-scale feature-fusion convolutional neural network (MSF-CNN), and cross-center generalization evaluation. Within this framework, MSF-CNN enhances the representation of stone-related features by fusing multi-scale and global contextual information and introducing a channel attention mechanism. We evaluated 1,496 coronal NCCT images from 497 subjects at a single center with institutional ethics approval, and additionally used 369 images from a publicly available external dataset as an independent external test set. Seven image enhancement methods were systematically compared, and ablation experiments were conducted under a unified preprocessing pipeline. The results showed that Laplacian sharpening achieved the best performance among the evaluated enhancement methods, yielding 99.16% accuracy and 99.97% AUC on the internal test set and 97.02% accuracy and 98.50% AUC on the external test set when combined with MSF-CNN. Overall, the proposed framework achieved highly accurate and cross-center-robust image-level classification of stone-positive and normal NCCT images, providing a potential auxiliary screening tool for clinical decision support.",
  "abstract_shown": true,
  "affiliations": [
    "The School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 10083, China.",
    "Department of Urology, Shenzhen Bao'an Shiyan People's Hospital, Shenzhen, Guangdong, 518000, China. doctorDenghuan@126.com."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hanlin Gao"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongyao Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Huan Deng"
    },
    {
      "affs": [
        0
      ],
      "name": "Weiping Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chao Yao"
    },
    {
      "affs": [
        0
      ],
      "name": "Chenjing Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4nxxttp4vqct6.bib",
  "cited_by": 0,
  "doi": "10.1186/s12882-026-05228-x",
  "fulltext_url": "https://europepmc.org/articles/PMC13721688",
  "id": "4nxxttp4vqct6",
  "issns": [
    "1471-2369"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958077",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2740|Kidney Stones and Urolithiasis Treatments"
    ]
  ],
  "openalex": "W7171945820",
  "pdf_url": "https://europepmc.org/articles/PMC13721688?pdf=render",
  "pmcid": "PMC13721688",
  "pmid": "42958077",
  "published": "2026-07-31",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/4nxxttp4vqct6.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:47Z",
      "native_id": "MED:42958077",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958077"
    },
    {
      "first_seen": "2026-11-03T12:22:20Z",
      "native_id": "PMC13721688",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12882-026-05228-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2740|Kidney Stones and Urolithiasis Treatments",
      "subfield": "general"
    }
  ],
  "title": "Multi-scale feature fusion for image-level kidney stone classification on NCCT images: a cross-center generalization evaluation.",
  "title_text": "Multi-scale feature fusion for image-level kidney stone classification on NCCT images: a cross-center generalization evaluation.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:20Z",
  "url": "https://research.draconitic.com/paper/4nxxttp4vqct6",
  "venue": "BMC nephrology",
  "versions": []
}