Cochlear implants (CIs) restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. The high electrical conductivity inside the cochlea, however, causes the electrical stimulus to spread, reducing speech comprehension. Understanding the stimulus spread in an individual patient is hampered by the poor accessibility of the inner ear and by the lack of suitable in vitro or in vivo models. Here, we report on the development of a neural network model that is informed by measurements in 3D printed biomimetic cochleae with realistic conductivity and anatomy. Using four geometric features captured in patients’ pre-operative CT scans, the model can reconstruct patients’ clinical electric field imaging (EFI) profiles arising from off-stimulation positions with a 90% mean accuracy (n=6). Moreover, the model informs the stereotypical cochlear geometries prone to stimulus spread and replicates the uncommon clinical occurrence of the ‘mid-dip’ EFI profiles. This work enables on-demand printing of patient-relevant cochlear models for CI testing, and directly reveals individual patient’s in vivo cochlear tissue resistivity (0.6 – 15.9kΩcm, n=16) by CI telemetry.