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This paper details the creation and validation of a patient-specific digital twin for planning ventriculoperitoneal shunt trajectories, addressing the high failure rate of freehand insertion. The twin reconstructs patient anatomy from CT scans and computationally identifies optimal entry points and angles to maximize intraventricular catheter length while adhering to safety constraints. In a prospective study, this method achieved significantly higher rates of Grade 1 shunt placement compared to historical controls, with reduced entry and angular deviations.
A patient-specific digital twin dramatically improves VP shunt trajectory planning, achieving near-perfect Grade 1 placement in a prospective trial where freehand insertion historically failed 90% of the time.
Catheter obstruction is the commonest cause of shunt failure; freehand insertion at Keen's point leaves the tip free in cerebrospinal fluid in only one case in ten. We built a digital twin that reconstructs skull, scalp, ear and ventricles from preoperative CT by deterministic geometry, searches every entry around Keen's point for the perpendicular trajectory with the longest intraventricular length under safety constraints, and returns the plan as two ruler measurements from the ear and two depths. Executed freehand in 11 consecutive adults (IDEAL Stage 2a), it achieved Hayhurst Grade 1 placement in 10 of 11 against 22 of 213 historically (p = 9 x 10^-9), median entry and angular deviations 2.5 mm and 5.9 degrees, 1.4 cm more intraventricular catheter than Keen's point, and no intraoperative complication. The technique was unchanged; the gain came from choosing entry and depth per patient. Whether it lowers revision is the next stage's hypothesis.