Lessons learned from the 2023 Türkiye twin earthquakes: how radiology facilities can respond to disasters

On 6 February 2023, back-to-back earthquakes (Mw 7.7 and 7.6) in southeastern Türkiye produced a prolonged mass-casualty incident, placing heavy demands on radiology services nationwide. Operational and clinical lessons were derived from frontline experience to guide emergency imaging under disaster conditions. Surge rostering and rapid staff redistribution were implemented; point-of-care ultrasound was employed for damage-control triage; and "CT-first" pathways were adopted for polytrauma. Workflow continuity during PACS/network outages was maintained with paper logs, downtime order sets, and standardized critical findings phrasing. Disaster teleradiology was activated through streamlined credentialing and ID-agnostic reporting, enabling rapid capacity expansion and support for hospitals beyond the epicentral region. Dominant injury patterns observed were crush injury with acute compartment syndrome; traumatic brain injury; chest trauma with sequential rib fractures, pulmonary contusion and pneumothorax; lumbar-predominant spinal fractures; and pelvic and long-bone injuries. Imaging correlates of rhabdomyolysis and non-lethal pneumomediastinum compatible with the Macklin effect are summarized. Protocol adaptations that preserved throughput and safety included abbreviated, low-dose trauma CT; cautious contrast use in renally vulnerable patients; and corridor-based scanner prioritisation during overflow. On the basis of these observations, a radiology preparedness bundle is presented: redundant power and IT failover; predefined modality-prioritisation matrices; caches of portable US/X-ray for secondary triage; streamlined trauma CT checklists; templated reports that foreground immediate actions; and interoperable teleradiology hooks that can be switched on rapidly. These measures are transferable to earthquakes and other mass-casualty events, sustaining diagnostic capacity, communication, and team alignment under extreme constraints.