Reconstruction of the anterior maxillary defect using a vascularized bone flap with residual teeth in the posterior maxilla

BACKGROUND

Microsurgical bone reconstruction is challenging in cases of anterior maxillary defects because of the presence of residual teeth. The study analyzed the method and feasibility of using vascularized bone flaps to reconstruct anterior maxillary defects.

METHODS

We retrospectively analyzed 15 patients treated at the Department of Oral and Maxillofacial Surgery, Peking University School, and Hospital of Stomatology between November 2017 and January 2023.

RESULTS

A total of 14 patients were successfully reconstructed using the digital technique: 6 deep circumflex iliac artery (DCIA) flaps and 8 fibular free flaps. Palatal and buccal pedicle paths were used in 4 and 10 cases, respectively, for vascular anastomosis. The implant coverage rates of the DCIA and fibular free flaps were 87.2% and 92.6%, respectively (p > 0.05).

CONCLUSION

Anterior maxillary defects reconstructed with the DCIA and fibular free flaps are reliable methods. Furthermore, the palatal and buccal paths are recommended for vascular anastomosis.

[Application of near-infrared autofluorescence imaging-based convolution neural network in recognition of parathyroid gland]

Objective: To investigate the application value of near-infrared autofluorescence imaging-based convolution neural network (CNN) for automatic recognition of parathyroid gland. Methods: The data of 83 patients who underwent thyroid papillary cancer surgery in the Department of Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University from August 2020 to March 2022 were retrospectively analyzed, and a total of 725 autofluorescence images of parathyroid gland were collected during the surgery. Meanwhile, non-parathyroid fluorescence imaging videos in the operation area of 10 patients were also collected, and 928 non-parathyroid fluorescence images were captured from those videos. The fluorescence images of parathyroid and non-parathyroid glands were directly used as input features for deep learning to construct ResNet 34, VGGNet 16 and GoogleNet models for automatic parathyroid identification. The ability of different models to identify parathyroid glands was tested by indicators such as accuracy, specificity, sensitivity, precision, receiver operating characteristic curve and area under the curve (AUC). In addition, 30 fluorescence images of parathyroid and 35 fluorescence images of non-parathyroid glands in 13 patients with papillary thyroid cancer from March to May 2022 were collected to prospectively test the best performing CNN model. Results: Among the 83 patients, there were 25 males and 58 females, with the mean age of (46.7±12.4) years. In the binary classification (parathyroid gland and non-parathyroid gland), the ResNet 34 model performed the best in different CNN models, the accuracy, specificity, sensitivity and precision of the identification test set were 97.6%, 96.3%, 99.3% and 95.5%, and the AUC reached 0.978 (95%CI: 0.956-0.991). In the prospective test, the prediction accuracy of the ResNet 34 model reached 93.8%, and the AUC was 0.938 (95%CI: 0.853-0.984). Conclusion: The near-infrared autofluorescence imaging-based deep CNN has good application value in the automatic recognition of parathyroid gland, and can be used to assist the recognition and protection of parathyroid gland in thyroid cancer surgery.