Skeletal Muscle Index Calculation (SMIC): Automated Sarcopenia Assessment
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Precise Muscle Mass Analysis
Automatically calculate skeletal muscle index from CT images at the L3 vertebral level to assess sarcopenia and predict clinical outcomes.
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Key Features
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Automatically identify and segment skeletal muscle at the L3 vertebral level using deep learning algorithms trained on thousands of CT scans.
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Calculate skeletal muscle index (SMI) normalized to patient height squared and compare to age and gender-specific reference values for sarcopenia assessment.
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Track changes in muscle mass over time with automated comparison reports to monitor treatment response, nutritional interventions, and disease progression.
Clinical Applications
Our SMIC technology supports multiple specialties in assessing sarcopenia and predicting clinical outcomes.
Oncology
Assess cancer cachexia and predict treatment tolerance, surgical outcomes, and overall survival in cancer patients.
Hepatology
Evaluate sarcopenia in cirrhosis patients to predict complications, hospitalization risk, and survival outcomes for transplant planning.
Geriatrics
Identify age-related sarcopenia and frailty to guide interventions and predict functional outcomes in elderly patients.
Critical Care
Assess muscle wasting in ICU patients to guide nutritional and rehabilitation interventions and predict recovery trajectories.
Clinically Validated Results
View Validation Studies95.8%
Accuracy compared to manual segmentation by radiologists
92%
Time reduction in muscle index calculation workflow
8+
Peer-reviewed publications validating our SMI algorithm
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See how Revelio's SMIC can integrate with your radiology workflow and improve patient care.
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