Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

Objective:To investigate the computed tomography (CT)-based integrated deep learning model for qualitative and quantitative classification of hepatic portal vein.Methods:The retrospective study was conducted. The CT imaging data of 291 patients undergoing upper-abdomen enhanced CT examination in the Beijing Tsinghua Changgung Hospital of Tsinghua University from October 2017 to January 2019 were collected. There were 195 males and 96 females, aged (51±12)years. The hepatic portal vein was reconstructed using the three-dimensional reconstruction system. Three-dimensional point cloud was input to the encoder model to obtain the three-dimen-sional reconstructed vectorized representation, which was used for qualitative classification and quantitative representation classification. Measurement data with normal distribution were repre-sented as Mean± SD, and comparison between groups was conducted using the paired t test. Count data were repre-sented as percentages or absolute numbers, and comparison between groups was analyzed using the paired chi-square test. Results:(1) Three-dimensional reconstruction of portal vein and anatomical classification. Three-dimensional structure was reconstructed in the 291 patients. Classification of main hepatic portal vein showed 211 cases of Akgul type A, 29 cases of Akgul type B, 16 cases of Akgul type C, 10 cases of Akgul type D, and 25 cases of unclassifiable. (2) Prediction of qualitative classification of main hepatic portal vein. Of the 291 patient samples, 25 unclassifiable or poor quality samples were excluded, 266 samples were used for automated qualitative classification of the main portal vein by machine model. There were 211 cases of Akgul type A, 29 cases of Akgul type B, 26 cases of Akgul type C&D. The Macro-F1 of 266 patients was 61.93%±40.50% and the accuracy was 84.99%, versus 32.38%±19.81% and 61.65% of Random classifier, showing significant differ-ences between them ( t=7.85, χ2=62.89, P<0.05). (3) Quantitative representation of portal vein classification. The probabilities of quantitative classification for Akgul qualitative classification of similar samples included P@1 as 73%±45%, P@3 as 70%±37%, P@5 as 69%±35%, P@10 as 67%± 32%, mean reciprocal rank(MRR) as 80%±34%, versus 57%±50%, 58%±35%, 58%±32%, 58%± 30%, 70%±37% of the baseline model, showing significant differences between the two analytical methods ( t=5.22, 5.11, 5.00, 4.99, 3.47, P<0.05). Conclusion:The automated classification model for the hepatic portal vein structure was constructed using CT-based three-dimensional reconstruc-tion and deep learning technology, which can achieve automatic qualitative classification and quanti-tatively describe the hepatic portal vein structure.