Background: User-friendly tools for assessing nutrition status and interventions in malnourished patients are crucial. This study evaluated the effectiveness of a personalised nutrition intervention using a novel oral nutritional supplement and AI-supported morphofunctional assessment to monitor clinical outcomes in patients with disease-related malnutrition (DRM). Methods: This prospective observational study involved patients receiving concentrated high-protein, high-calorie ONS (cHPHC-ONS), per usual clinical practice. Comprehensive assessments were performed at baseline (B0) and three months (M3) post-intervention. Results: 65 patients participated in the study. Significant decreases were observed in the percentage weight loss from B0 (-6.75 ± 7.5%) to M3 (0.5 ± 3.48%) (p < 0.01), in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01), severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01), and sarcopenia (B0: 19.4%; M3: 15.5%; p < 0.04). Muscle area increased (p = 0.03), and there were changes in the echogenicity of the rectus femoris muscle (p = 0.03) from B0 to M3. In patients aged ≥60, an increase in muscle thickness (p = 0.04), pennation angle (p = 0.02), and handgrip strength (p = 0.04) was observed. There was a significant reduction in the prevalence of malnutrition (B0: 93.4%; M3: 78.9%; p < 0.01) and severe malnutrition (B0: 60.7%; M3: 40.3%; p < 0.01). Conclusions: In patients with DRM, a personalised intervention with cHPHC-ONS significantly reduces the prevalence of malnutrition, severe malnutrition, and sarcopenia and improves muscle mass and function.
Keywords: artificial intelligence; malnutrition; nutritional support; sarcopenia.