AI Article Synopsis
- Osteotomies during rhinoplasty usually rely on the surgeon's instinct to decide on the amount and direction of force applied.
- The study aimed to identify when fractures occur and when the osteotome hits the thicker frontal bone by using an instrumented hammer to record impact forces on human specimens.
- A machine learning algorithm successfully predicted fracture occurrences and the proximity to the frontal bone with high accuracy rates (up to 93%) and provided real-time feedback for surgeons.
Article Abstract
Osteotomies during rhinoplasty are usually based on surgeon's proprioception to determine the number, energy, and trajectory of impacts. The first objective was to detect the occurrence of fractures. The second objective was to determine when the thicker frontal bone was encountered by the osteotome. An instrumented hammer was used to measure the impact force during lateral osteotomies on nine human anatomic specimens. A prediction algorithm was developed using machine learning techniques, to detect the occurrence of fractures, and the proximity of the osteotome to the frontal bone. The algorithm was able to predict the occurrence of fractures and the proximity to the frontal bone with a prediction rate of 83%, 91%, and 93% when allowing for an error of 0, 1, and 2 impacts, respectively. The location of the osteotome in the frontal bone was predicted with an error of 7.7%. An osteotomy hammer measuring the impact force when performing lateral osteotomies can predict the occurrence of fractures and the proximity to the frontal bone, providing the surgeon with instant feedback.
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| http://dx.doi.org/10.1089/fpsam.2021.0107 | DOI Listing |
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