{
  "id": 1028,
  "url": "https://arxiv.org/abs/2606.15250v1",
  "title": "Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs",
  "summary": "Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty. Traditional measurement methods are manual and time-consuming, while recent machine learning approaches typically rely on locating a fixed set of anatomical landmarks. This dependence limits flexibility and may require re-annotation when clinical definitions change. To address this, we propose an automated workflow using Implicit Neural Shape Functions ",
  "authors": "Zhisen Hu, Antti Kemppainen, David Johnson, Egor Panfilov, Huy Hoang Nguyen, Timothy Cootes et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-13T11:10:27.000Z",
  "fetched_at": "2026-07-14T14:14:59.013Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1028",
  "original_url": "https://arxiv.org/abs/2606.15250v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}