Use of a Novel Artificial Intelligence Tool for Evaluating Primary Stability and Immediate Loading Suitability of Dental Implants: An In Vitro Pilot Study.

Marco Degidi, Giuseppe Daprile
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Abstract

Purpose: To evaluate the correspondence between output from a new artificial intelligence tool (AIT) and clinician evaluation regarding the immediate loading suitability of dental implants based on insertion torque curves recorded during implant placement in an in vitro test. The secondary aim was to analyze peak insertion torque (PIT) and variable torque work (VTW) values of the implants.

Materials and methods: The study was performed with four different densities of artificial bone blocks of solid rigid polyurethane without a cortical layer. Five types of implants with different macrogeometries were used. A total of 140 implants (7 implants of each type in the four polyurethane blocks) were inserted. Immediately after implant placement, the insertion torque curves were classified by the operator as suitable (S) or nonsuitable (NS) for immediate loading. The same curves were then analyzed by the new AIT, which classified them as belonging to the 'YES' or 'NO' class. For each implant, PIT and VTW values were also recorded.

Results: The correspondence between clinician and AIT evaluation was 99.3%, with only one false negative reported by the algorithm analysis. The AIT was found to have a sensitivity of 98.95%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 97.8%. Mean PIT of the whole sample was 34.19 ± 19.43 Ncm, while mean VTW was 2,266.89 ± 1,993.73 Ncm. Statistically significant differences were found between implant systems in the whole sample and according to density of the polyurethane block.

Conclusions: The AIT showed a high level of accuracy in the prediction of immediate loading suitability of dental implants based on the provided insertion torque curves. All the implants used in the in vitro test achieved good levels of primary stability, except when inserted in the least-dense polyurethane block. Clinical studies conducted with larger samples and more clinicians are necessary to confirm these results.

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使用人工智能驱动的新型工具评估牙科植入物的初步稳定性和即时加载可行性:一项体外试验研究。
目的:本研究的主要目的是评估人工智能驱动的新工具预测与临床医生在体外测试中植入物插入过程中记录的曲线的即时负载适用性评估之间的对应性。第二个目的是分析用于体外研究的植入物的峰值插入转矩(pIT)和可变转矩功(VTW)值。材料和方法:研究了四种不同密度的无皮层硬质聚氨酯人工骨块。使用了五种具有不同宏观几何形状的植入物。总共插入了140个植入物(四个聚氨酯块中每种类型的7个植入物)。植入物放置后,操作者立即将插入曲线分类为适合或不适合立即加载。在第二个时刻,新的AIT对相同的曲线进行了分析,将它们分类为YES或NO类。对于每个植入物,还记录pIT和VTW。结果:外科医生和AIT评估之间的一致性为99,3%,算法分析仅报告一个假阴性。灵敏度为98.95%,特异性为100%,阳性预测值为100%,阴性预测值为97.8%。整个样品的平均pIT为34.19±19.43Ncm,平均VTW为2266.89±1993.73Ncm。在整个样品中的植入物系统之间以及除以聚氨酯块密度时,发现了统计学上的显著差异。结论:AIT在预测插入曲线的即时负荷适用性方面显示出较高的准确性。体外测试中使用的所有植入物都能够达到良好的初级稳定性水平,不包括插入密度较低的聚氨酯嵌段时。需要在更大的样本中进行临床研究,并有更多的外科医生参与,以证实这些结果。
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