人工智能在板层角膜成形术中的应用。

IF 0.8 4区 医学 Q4 OPHTHALMOLOGY Klinische Monatsblatter fur Augenheilkunde Pub Date : 2024-06-01 Epub Date: 2024-03-19 DOI:10.1055/a-2290-5373
Sebastian Siebelmann, Takahiko Hayashi, Mario Matthaei, Björn O Bachmann, Johannes Stammen, Claus Cursiefen
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引用次数: 0

摘要

人工智能(AI)培训正变得越来越流行。关于板层角膜移植术的研究也越来越多。尤其是光学相干断层扫描的无创和高分辨率成像技术,注定了板层角膜成形术是人工智能的应用领域。虽然人工智能在技术上很容易实现,但关于使用人工智能优化板层角膜成形术的研究却寥寥无几。现有的研究主要集中在 DMEK 和 DSAEK 中反泡概率的预测及其移植物粘附性,以及 DALK 中大泡的形成。此外,利用人工智能技术还可以自动记录常规参数,如角膜水肿、内皮细胞密度或移植物脱离的大小。利用人工智能优化板层角膜移植术潜力巨大。不过,已发布的算法也有局限性,因为它们只能在一定程度上在不同中心、外科医生和不同设备制造商之间进行移植。
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Artificial Intelligence for Lamellar Keratoplasty.

The training of artificial intelligence (AI) is becoming increasingly popular. More and more studies on lamellar keratoplasty are also being published. In particular, the possibility of non-invasive and high-resolution imaging technology of optical coherence tomography predestines lamellar keratoplasty for the application of AI. Although it is technically easy to perform, there are only a few studies on the use of AI to optimise lamellar keratoplasty. The existing studies focus primarily on the prediction probability of rebubbling in DMEK and DSAEK and on their graft adherence, as well as on the formation of a big bubble in DALK. In addition, the automated recording of routine parameters such as corneal oedema, endothelial cell density or the size of the graft detachment is now possible using AI. The optimisation of lamellar keratoplasty using AI holds great potential. Nevertheless, there are limitations to the published algorithms, in that they can only be transferred between centres, surgeons and different device manufacturers to a limited extent.

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CiteScore
1.30
自引率
0.00%
发文量
235
审稿时长
4-8 weeks
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