Error assessment and correction for extrusion-based bioprinting using computer vision method

Bioprinting offers a new approach to addressing the organ shortage crisis. Despite recent technological advances, insufficient printing resolution continues to be one of the reasons that impede the development of bioprinting. Normally, machine axes movement cannot be reliably used to predict material placement, and the printing path tends to deviate from the predetermined designed reference trajectory in vary-ing degrees. Therefore, a computer vision-based method was proposed in this study to correct trajectory deviation and improve printing accuracy. The image algorithm calculated the deviation between the printed trajectory and the reference trajectory to generate an error vector. Furthermore, the axes trajectory was modified according to the normal vector approach in the second printing to compensate for the devia-tion error. The highest correction efficiency that could be achieved was 91%. More significantly, we discovered that the correction results, for the first time, were in a normal distribution instead of a random distribution.