V5 Diagnostic Ultrasound System, H5 Diagnostic Ultrasound System, XV5 Diagnostic Ultrasound System, XH5 Diagnostic Ultrasound System, V4 Diagnostic Ultrasound System, H4 Diagnostic Ultrasound System, XV4 Diagnostic Ultrasound System, XH4 Diagnostic Ultrasound System

The V5/H5/XV5/XH5, V4/H4/XV4/XH4 diagnostic ultrasound systems are general-purpose, mobile, software-controlled ultrasound devices that acquire and display ultrasound images in multiple modes (2D, Doppler, M-mode, 3D/4D, etc.). They are intended for diagnostic imaging across diverse clinical applications including obstetrics, cardiology, abdominal, gynecology, pediatrics, and musculoskeletal imaging, used by trained healthcare professionals in hospitals, clinics, and private practices.

The proposed devices employ the same fundamental scientific technology as the primary predicate (V8/XV8/XH8, V7/XV7/XH7, V6/XV6/XH6). Key differences include: addition of one new transducer (PA1-5AE), inclusion of dermatology as a clinical application (already cleared in HM70 EVO), addition of a WiFi module, and inclusion of previously cleared AI-assisted features (BiometryAssist, HeartAssist, ViewAssist, UterineAssist, NerveTrack) without modifications.

IEC 60601-1 and IEC 60601-1-2 (electrical safety and EMC), IEC 60601-2-37 (ultrasonic equipment), ISO 10993-1 (biocompatibility), ISO 14971 (risk management), NEMA UD 2-2004 (acoustic output measurement), and IEC TR 60601-4-2 (EMC guidance).

Substantial equivalence is established because both devices share identical intended use (diagnostic ultrasound imaging across the same clinical applications), identical imaging modes and capabilities, same design principles compliant with electrical/physical safety standards, equivalent acoustic power levels below FDA limits, and the new features (one transducer, dermatology application, WiFi, and five AI tools) are either already cleared in predicates or borrowed from cleared devices without modification. Comprehensive non-clinical testing including acoustic output, biocompatibility, software validation, and deep-learning algorithm performance (with high accuracy/dice-score results on diverse patient populations) demonstrates safety and performance equivalence.