Landmarks restore,a machine learning algorithm-aided surgical planning for cross-midline maxillo-mandibular defect

doi:10.19438/j.cjoms.2020.04.007

China Journal of Oral and Maxillofacial Surgery ›› 2020, Vol. 18 ›› Issue (4): 323-327.doi: 10.19438/j.cjoms.2020.04.007

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Landmarks restore,a machine learning algorithm-aided surgical planning for cross-midline maxillo-mandibular defect

ZHOU Zi-jie¹, ZHU Xiang-yang², HAN Jing¹, LIU Jian-nan¹, ZHANG Chen-ping¹

1. Department of Oromaxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology. Shanghai 200011;
2. Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University. Shanghai 200240, China

Received:2020-02-05 Online:2020-07-20 Published:2020-09-10

Abstract

Abstract: PURPOSE: The distance and angles among characteristic points of normal Chinese jaws were measured and analyzed by an algorithm to explore the morphological correlation between maxilla and mandible,and to guide a personalized preoperative design for cross-midline defects. METHODS: A total of 111 normal maxillary CT data (Dicom format) were collected and delivered to surgical planning software(Proplan CMF 3.0),which included 43 males and 68 females. Each case had 16 key points to mark. The data were analyzed using Mathematical software(MATLAB). The data from male and female jaw bones were analyzed using SPSS 22.0 software package to analyze the difference between genders. RESULTS: There was significant difference (P<0.05) in the size of jaw bones between genders, but no significant difference in angles except for ∠b₁ab₂(males 136.06°,females 132.18°,P<0.05 ). A machine learning algorithm was programmed based on vector matching model, and it was clinically applied to a patient with cross-midline mandibular defect. CONCLUSIONS: With the benefit of algorithm-aided preoperative design, a more convenient way can be achieved to tailor a similar maxilla or mandible for patients, especially in cases with cross-midline defects.

Key words: Maxillo-mandibular reconstruction, Anatomic landmarks, Machine learning, Preoperative planning

CLC Number:

R782.2

ZHOU Zi-jie, ZHU Xiang-yang, HAN Jing, LIU Jian-nan, ZHANG Chen-ping. Landmarks restore,a machine learning algorithm-aided surgical planning for cross-midline maxillo-mandibular defect[J]. China Journal of Oral and Maxillofacial Surgery, 2020, 18(4): 323-327.

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Landmarks restore,a machine learning algorithm-aided surgical planning for cross-midline maxillo-mandibular defect

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