Machine learning algorithm based on jaw feature points assist complex maxillary and mandibular reconstruction - 08/02/23

Doi : 10.1016/j.jormas.2022.11.019

Jing Han ^a,^{^{#
Jing Han and Zijia Liu contributed equally to this work as co-first author.}}, Zijia Liu ^b,^{^{#
Jing Han and Zijia Liu contributed equally to this work as co-first author.}}, Zijie Zhou ^a, Yige Liu ^a, Guangtao Zhai ^b,^⁎ , Jiannan Liu ^a,^⁎
^a Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
^b School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200241, China

^⁎Corresponding authors.^⁎⁎Corresponding authors.

Abstract

Purposes

Large-scale jaw reconstruction can hardly achieve satisfactory results only by relying on doctors’ experience. In this study, we assessed a new approach using a machine learning algorithm based on jaw feature points to assist complex jaw reconstruction in patients with maxillary and mandibular defects.

Methods

One hundred and two computed tomography (CT) data on the jaw were collected and 16 skeletal marker points on the jaw were selected. The machine learning algorithm learned the positional relationship between points and built a model, which was used to predict the coordinate position of an unknown point. Then the model was used for a surgical plan in clinical cases.

Results

The linear regression model based on machine learning can control the error within 3 mm. In linear models, Lasso has a slight advantage over the others. We used Lasso to predict the missing points for two patients with maxillary and mandibular defect, respectively. The operation was carried out as planned, and the defects were successfully repaired.

Conclusions

The restoration of jaw feature points based on a machine learning algorithm is expected to solve large-scale jaw defects without contralateral reference.

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Keywords : Machine learning algorithm, Jaw reconstruction, Jaw feature points