10-year mortality predicted by deep learning on chest radiographs

RadAISlice Newsletter

Weekly Updates in Radiology AI

Good morning, there. A deep learning model estimated skeletal muscle and fat mass from over 185,000 chest radiographs, predicting 10-year mortality.

I am struck by how this model reliably forecasts clinically meaningful outcomes using only routine CXRs and deep learning. The findings suggest that opportunistic body composition analysis may become a practical, low-cost risk stratification tool, especially in older adults. It shifts how I think about the role of radiology in preventive health and long-term care planning.

Would you trust AI-derived body composition measurements for guiding geriatric care?

Here's what you need to know about Radiology AI last week:

• Deep Learning Extracts Prognostic Body Composition from Chest X-rays

• FDA Clears Aidoc’s Comprehensive Abdominal CT Triage AI

• Unified Federal AI Policy Urged for Imaging Adoption

• Prospective Multicenter Validation of Deep Learning for Mitral Valve Disease

• Plus: 1 newly released dataset, 6 FDA approved devices & 4 new papers.

🔬 Deep Learning Extracts Prognostic Body Composition from Chest X-rays

RadAISlice: This research leverages chest x-rays to predict long-term mortality and frailty using deep learning.

The details:

• 107,568 CXR–BIA pairs for training, 77,655 for temporal validation

• Skeletal muscle mass predicted with r=0.967, MAE 1.40 kg

• Higher predicted muscle index linked to lower 10-year mortality (HR 0.65)

• Validation on 5,932 older adults and 925 geriatric clinic patients

• Performance exceeded BMI for sarcopenia and frailty associations

Key takeaway: AI-enabled CXR-based body composition estimation is accurate, prognostically meaningful, and scalable for routine risk stratification in older adults.

🛡️ FDA Clears Aidoc’s Comprehensive Abdominal CT Triage AI

Image from: Radiology Business

RadAISlice: FDA clearance enables unified AI triage for 14 abdominal CT indications, promising efficiency gains.

The details:

• Covers 11 new and 3 prior CT triage indications

• Mean sensitivity 97%, specificity 98% in pivotal study

• Targets emergency and outpatient abdominal CT workflow

• Can flag critical findings for radiologist review

Key takeaway: Comprehensive FDA-cleared AI tools mark a step toward integrated triage and streamlined acute abdominal imaging workflows.

🏥 Unified Federal AI Policy Urged for Imaging Adoption

Image from: Radiology Business

RadAISlice: A national policy could overcome AI deployment barriers in radiology and beyond.

The details:

• Survey of 27 major healthcare organizations

• Varied state-level rules seen as key obstacles

• Centralized rules would support broader AI trial and integration

• Radiology cited as a prime application area

Key takeaway: National regulatory harmonization may clear a path for broader, more confident clinical adoption of AI in radiology, improving care delivery.

🩺 Prospective Multicenter Validation of Deep Learning for Mitral Valve Disease

RadAISlice: Multicenter, external validation demonstrates AI's robustness for echocardiographic mitral valve classification.

The details:

• 6,606 TTE exams from multiple nationwide sites

• External test AUROC 0.931–0.992 by etiology

• Consistency across MR severity, image quality strata

• Detects normal, rheumatic, degenerative, prolapse, functional types

Key takeaway: Externally validated echocardiography-AI for valvular disease moves imaging AI closer to scalable, generalizable clinical utility.

RECIST-CT (2026-01-08)

Modality: CT | Focus: thorax, abdomen | Task: segmentation, measurement

• Size: 58 scans from 22 patients (~1,246 lesions annotated)

• Annotations: manual 3D instance segmentations of all tumors and lymph nodes; 82 RECIST-compliant diameter measurements

• Institutions: University of Chile, Universidad de Concepción et al.

• Availability:

  public (Zenodo link)

• Highlight: First public CT dataset with full lesion segmentations (primary/metastases/nodes) plus RECIST 1.1 measurements.

🏛️ FDA Clearances

• K252970 - Aidoc's BriefCase-Triage CARE Multi-triage CT Body AI cleared for radiologist-prioritized urgent case flagging across abdominal CT.

• K253639 - GE Healthcare secures clearance for View, a new image processing system enhancing radiology image interpretation.

• K253366 - LOGIQ Fortis ultrasound, cleared for real-time blood flow assessment and tissue visualization to support broader clinical diagnostics.

• K253370 - LOGIQ Totus ultrasound platform cleared for advanced pulsed Doppler imaging of blood flow and tissue structures.

• K253784 - 3DICOM MD Cloud gets 510(k) clearance, supporting web/cloud-based radiology image interpretation workflows.

• K253009 - DS Core Detect (Dentsply Sirona) cleared; assists clinicians with AI-based radiological image analysis.

• Explore last week's 8 radiology AI FDA approvals.

📄 Fresh Papers

• doi:10.64898/2026.01.13.26343927 - Multimodal ML models can predict continuous cognitive decline with partial generalizability across independent MRI cohorts.

• doi:10.64898/2026.01.13.26343950 - Large-cohort ML dissects how body composition and brain phenotypes independently and jointly shape cognitive aging trajectories.

• doi:10.1016/j.media.2026.103954 - A new MS lesion segmentation approach demonstrates strong generalizability across MRI domains and missing contrasts.

• doi:10.64898/2026.01.13.26344036 - Analysis of a leading FCD detection AI pipeline reveals segmentation failures and imaging feature gaps as key error sources.

• Browse 139 new radiology AI studies from last week.

📰 Everything else in Radiology AI last week

• Mandated patient access to imaging reports has driven a 30% increase in radiologist workload, raising burnout concerns.

• A large review finds limited evidence that AI reduces radiologist burnout, with some data suggesting increased workload.

• Microsoft and Bristol Myers Squibb team to improve early lung cancer detection using scalable AI-based radiology tools.

• AI analysis of abdominal CT demonstrates muscle density as a key fall risk predictor in adults 45+, enabling early intervention.

• OpenEvidence hits a $12B valuation as US physician AI adoption surges, but ECRI cites chatbots as 2026's top healthcare hazard.

• Read 17 fresh radiology AI news articles for last week.

That's it for today!

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