{
  "abstract": "Although retinal fundus imaging is a non-invasive tool to assess systemic and fundus pathology, current deep learning algorithms are mostly single-task, lack clinical interpretability and are unable to handle acquisition variation between different domains. These constraints limit their clinical utility: when a patient comes to the clinic for fundus screening, they could also be diabetic, have glaucoma, AMD, and increased cardiovascular risk, with each disease having retinal vascular biomarkers that require a patient to be tested for in the clinic, which must be managed within a framework of common, interpretable, and generalizable models rather than a pipeline of independent models. To solve these problems, we suggest a multi-task deep learning network that simultaneously classifies DR severity (five-class), glaucoma likelihood (binary), AMD stage (four-class) and cardiovascular risk (binary) from a single retinal fundus image using a single forward pass. It adopts an EfficientNet-B4 convolutional backbone pretrained on the ImageNet dataset, a six-layer multi-head self-attention Transformer encoder (d_model = 512, h = 8), a novel dual-branch spatial-channel attention module that builds on, but significantly expands, the CBAM module, and four task-specific output heads that are optimized using the homoscedastic uncertainty-weighted multi-task loss following the formulation of Kendall et al. Per-task anatomical explanations are produced by back propagating the CNN backbone via Grad-CAM in the anatomical space. The proposed model was evaluated on EyePACS, APTOS 2019, Messidor-2 and RIM-ONE v3 and achieved an accuracy of 94.7%, AUC = 0.978, sensitivity = 93.2% and specificity = 95.8% while being statistically better than all single task models (DeLong test, p < 0.01, Bonferroni corrected). Under the reported experimental setting, the zero-shot cross-dataset evaluation achieves AUC ≥ 0.951, which is promising for its performance across datasets. Grad-CAM saliency maps are anatomically similar to established clinical grading criteria for each disease. The four task-specific heads share a single architecture and produce four outputs in one forward pass, but no individual patient in the training corpora had verified ground-truth labels for all four conditions simultaneously; the model is trained on partially labelled, multi-source data via a masked multi-task loss, and the cardiovascular output is an AVR-derived image-based surrogate rather than a validated clinical cardiovascular diagnosis. This proposed framework could be considered for an additional potential step toward the future when it comes to multi-disease fundus screening with an interpretable single-model solution, if it is assessed and approved at an independent multi-centre level and by regulatory bodies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Ravi Kumar Tenali"
    },
    {
      "name": "Anitha Potnuri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4czy7yykj8mj7.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-74871-z",
  "fulltext_url": null,
  "id": "4czy7yykj8mj7",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-74871-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7222815909",
  "pdf_url": "https://www.nature.com/articles/s41598-026-74871-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4czy7yykj8mj7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1038/s41598-026-74871-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-74871-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "A multi task deep learning framework for automated health condition prediction from retinal fundus images using hybrid attention and explainable AI",
  "title_text": "A multi task deep learning framework for automated health condition prediction from retinal fundus images using hybrid attention and explainable AI",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/4czy7yykj8mj7",
  "venue": "Scientific Reports",
  "versions": []
}