{
  "id": 3092731,
  "title": "Enhancing confidence in AI enhanced brain tumor imaging by measuring uncertainty",
  "url": "https://urgent.news/2026/08/24/enhancing-confidence-in-ai-enhanced-brain-tumor-imaging-by-measuring",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-24T19:00:09.000Z",
  "source": {
    "name": "Medical Xpress",
    "slug": "medical-xpress",
    "url": "https://medicalxpress.com/news/2026-08-confidence-ai-brain-tumor-imaging.html"
  },
  "original_language": "en",
  "account": "The study published in npj Digital Medicine aimed to enhance confidence in AI-enhanced brain tumor imaging by measuring uncertainty. Conventional tumor monitoring relies on subjective evaluation or simplified two-dimensional measurements, which can fail to accurately capture tumor growth patterns, particularly for slow or irregularly growing tumors such as meningiomas. Deep neural networks can segment tumors in 3D, but automated segmentation carries uncertainty that limits clinician trust. UCSF researchers addressed this issue by developing a deep learning framework that produced uncertainty estimates for meningioma segmentation on brain MRI, resulting in well-calibrated tumor volume measurements. Their Evidential Deep Learning (EDL) model achieved high accuracy and credible measurements, supporting safer deployment of clinical AI. The study focused on meningiomas, the most common primary brain tumor, with varying tumor borders and uncertainties depending on MRI characteristics. The researchers trained their framework on 1,655 MRIs from 788 patients, including postoperative ones to account for treatment-related changes. They also evaluated homogeneous and heterogeneous AI ensembles, assessing performance through spatial agreement between uncertainty maps and radiologist-identified ambiguous regions. The model showed high accuracy, with uncertainty maps aligning with ambiguous regions and well-calibrated volume estimates. External validation in 353 patients confirmed its generalizability. The researchers believe this calibrated uncertainty estimation can be applied to other tumor types, increasing trust in biomedical image segmentation. While trained on private data, the study's high performance on an independent test set suggests cross-institutional generalizability. However, future studies should incorporate multicenter datasets and multi-rater annotations to fully validate model uncertainty against human variability.",
  "summary": "MRIs are among the most accurate imaging tests used to diagnose brain tumors, and MRI-based monitoring of tumors guides critical treatment decisions. The clinical standard for measuring tumors often relies on either subjective evaluation or simplified two-dimensional (2D) measurements. However, for tumors with slow or irregular growth patterns, such as meningiomas, these 2D metrics can fail to…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}