Image-Based Quantitative Osteotomy Planning for Congenital Proximal Radioulnar Synostosis: Plan-Execution Discrepancy and Postoperative Radiographic Outcomes.

BACKGROUND: Proximal radioulnar synostosis is a rare congenital anomaly that restricts forearm rotation and significantly impairs daily function. Surgical correction typically involves radial osteotomy with shortening and derotation; however, planning of osteotomy level, bone shortening, and rotational correction remains largely subjective and relies heavily on surgeon experience. This study retrospectively evaluated an image-based quantitative planning framework designed to provide objective geometric parameters for osteotomy in congenital proximal radioulnar synostosis.

METHODS: Preoperative anteroposterior and lateral radiographs from eight unilateral cases were reconstructed to derive patient-specific deformity geometry, central bone axes, cut points, predicted cutting angles, and model-estimated radial shortening. The planning algorithm uses polynomial fitting and calculus-based minimization to identify osteotomy points. These predictions were compared with intraoperative measurements and postoperative radiographic and functional outcomes.

RESULTS: All procedures were complication-free, and seven of eight forearms showed improved rotational range. At final follow-up, two forearms had concentric radial head alignment, whereas six showed mild subluxation or dislocation. Observationally, larger discrepancies between model-estimated and surgeon-applied shortening were associated with suboptimal postoperative radial head alignment. In one representative case, a substantial discrepancy corresponded with progressive radial head dislocation despite preserved clinical motion.

CONCLUSION: This study highlights the potential value of quantitative, image-based modeling to support osteotomy planning by providing transparent, reproducible parameters that may reduce variability in surgical decision-making. Although limited by its small sample size, retrospective design, and reliance on radiographs, this work establishes a foundation for future prospective validation of computational decision-support tools in pediatric orthopedic deformity correction.

LEVEL OF EVIDENCE: Level IV.