Private Practice, Ludwing-Wilhelm Strasse, 17, Baden-Baden, Germany.
Lecturer, Academic Teaching and Research Institution of Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.
BMC Oral Health. 2020 Mar 19;20(1):80. doi: 10.1186/s12903-020-1062-4.
Artificial intelligence (AI) is a branch of computer science concerned with building smart software or machines capable of performing tasks that typically require human intelligence. We present a protocol for the use of AI to fabricate implant-supported monolithic zirconia crowns (MZCs) cemented on customized hybrid abutments.
The study protocol consisted of: (1) intraoral scan of the implant position; (2) design of the individual abutment and temporary crown using computer-aided design (CAD) software; (3) milling of the zirconia abutment and the temporary polymethyl-methacrylate (PMMA) crown, with extraoral cementation of the zirconia abutment on the relative titanium bonding base, to generate an individual hybrid abutment; (4) clinical application of the hybrid abutment and the temporary PMMA crown; (5) intraoral scan of the hybrid abutment; (6) CAD of the final crown with automated margin line design using AI; (7) milling, sintering and characterisation of the final MZC; and (8) clinical application of the MZC. The outcome variables were mathematical (quality of the fabrication of the individual zirconia abutment) and clinical, such as (1) quality of the marginal adaptation, (2) of interproximal contact points and (3) of occlusal contacts, (4) chromatic integration, (5) survival and (6) success of MZCs. A careful statistical analysis was performed.
90 patients (35 males, 55 females; mean age 53.3 ± 13.7 years) restored with 106 implant-supported MZCs were included in the study. The follow-up varied from 6 months to 3 years. The quality of the fabrication of individual hybrid abutments revealed a mean deviation of 44 μm (± 6.3) between the original CAD design of the zirconia abutment, and the mesh of the zirconia abutment captured intraorally at the end of the provisionalization. At the delivery of the MZCs, the marginal adaptation, quality of interproximal and occlusal contacts, and aesthetic integration were excellent. The three-year cumulative survival and success of the MZCs were 99.0% and 91.3%, respectively.
AI seems to represent a reliable tool for the restoration of single implants with MZCs cemented on customised hybrid abutments via a full digital workflow. Further studies are needed to confirm these positive results.
人工智能（AI）是计算机科学的一个分支，致力于构建能够执行通常需要人类智能的任务的智能软件或机器。我们提出了一种使用人工智能制造固位体支持的整体氧化锆冠（MZC）并粘结在定制混合基台上的方案。
研究方案包括：（1）种植体位置的口内扫描；（2）使用计算机辅助设计（CAD）软件设计个体基台和临时冠；（3）氧化锆基台和临时聚甲基丙烯酸甲酯（PMMA）冠的铣削，将氧化锆基台在相对的钛粘结基底外粘结，生成个体混合基台；（4）混合基台和临时 PMMA 冠的临床应用；（5）混合基台的口内扫描；（6）使用 AI 进行自动边缘线设计的最终冠的 CAD；（7）最终 MZC 的铣削、烧结和特性；（8）MZC 的临床应用。观察变量包括数学变量（个体氧化锆基台制造质量）和临床变量，如（1）边缘适合性质量，（2）邻面接触点质量，（3）咬合接触点质量，（4）颜色整合，（5）存活率和（6）MZC 的成功率。对数据进行了仔细的统计分析。
本研究纳入了 90 名患者（35 名男性，55 名女性；平均年龄 53.3±13.7 岁）共 106 个种植体支持的 MZC。随访时间为 6 个月至 3 年。个体混合基台制造质量的测量结果显示，氧化锆基台原始 CAD 设计与最终临时修复体时口内捕获的氧化锆基台网格之间存在 44μm（±6.3）的平均偏差。交付 MZC 时，边缘适合性、邻面和咬合接触质量以及美学整合效果均极佳。MZC 的 3 年累积存活率和成功率分别为 99.0%和 91.3%。
AI 似乎是一种可靠的工具，可通过全数字化工作流程为定制混合基台上粘结的单个种植体修复提供 MZC。需要进一步的研究来证实这些积极的结果。
