From prediction to clinical decision-making: Explainable machine learning in acute suppurative cholecystitis

Table 2 Clinical decision framework integrating explainable machine learning in acute suppurative cholecystitis

Risk level (ML output)	Explainability insight (SHAP)	Clinical interpretation	Recommended action	Multidisciplinary involvement
High risk	Strong contribution from radiomic heterogeneity-inflammatory markers	Likely suppurative cholecystitis with high operative complexity	Early laparoscopic cholecystectomy; prioritize OR scheduling; consider senior surgical team	Surgeon, anesthesiologist, radiologist, ICU team
Intermediate risk	Mixed feature contribution; moderate radiomic signal with variable clinical parameters	Uncertain progression; potential for deterioration	Close monitoring; repeat imaging; optimize comorbidities; reassess within 12-24 hours	Surgeon, radiologist, ward team
Low risk	Minimal radiomic heterogeneity; low inflammatory contribution	Likely uncomplicated disease	Conservative management; antibiotics; delayed surgery if indicated	Surgeon, ward team
High risk with discordant clinical findings	High radiomic signal but mild clinical presentation	Early-stage suppuration or subclinical progression	Escalate monitoring; consider early intervention despite mild symptoms	Surgeon, radiologist
Low risk with severe clinical presentation	Low radiomic signal but strong clinical/Lab abnormalities	Possible alternative diagnosis or false-negative model output	Re-evaluate diagnosis; additional imaging; do not rely solely on model	Full team

SHAP: SHapley Additive exPlanations; ML: Machine learning; ICU: Intensive care unit; OR: Operating room.