Dundar Tolga Turan, Pehlivanoğlu Meltem Kurt, Eker Ayşe Gül, Albayrak Nur Banu, Mutluer Ahmet Serdar, Yurtsever İsmail, Doğan İhsan, Duru Nevcihan, Türe Uğur
Department of Neurosurgery, Bezmialem Vakif University, Istanbul, Turkey.
Department of Biostatistics and Medical Informatics, Istanbul University-Cerrahpasa, Istanbul, Turkey.
Neurosurg Rev. 2025 Feb 19;48(1):251. doi: 10.1007/s10143-025-03345-z.
The relatively complex functional anatomy of the mediobasal temporal region makes surgical approaches to this area challenging. Several studies describe various surgical approaches, along with their combinations and modifications, to reach lesions of this region. Some of these surgical approaches have been compared using artificial intelligence-based approaches that can be predicted, classified, and analyzed for complex data. Several surgical approaches, such as anterior transsylvian, trans-superior temporal sulcus, trans-middle temporal gyrus, subtemporal-transparahippocampal, presigmoid-retrolabyrinthine, supratentorial-infraoccipital, and paramedian supracerebellar-transtentorial, were selected for comparison. Magnetic resonance images (MRIs) were taken according to the criteria specified by the Radiology Department. With an open-source software tool, volumetric data from cranial MRIs were segmented and anatomical structures in the main regions were reconstructed. The Q-learning algorithm was used to find pathways similar to these standard surgical pathways. The Q-learning scores among the selected pathways are as follows: anterior transsylvian (Q_A) = 31.01, trans-superior temporal sulcus (Q_B) = 25.00, trans-middle temporal gyrus (Q_C) = 28.92, subtemporal-transparahippocampal (Q_D) = 23.51, presigmoid- retrolabyrinthine (Q_E) = 27.54, supratentorial-infraoccipital (Q_F) = 27.2, and paramedian supracerebellar-transtentorial (Q _G) = 21.04. The Q-value score for the supracerebellar transtentorial approach was the highest among the examined approaches and therefore optimal. A difference was also found between the total risk score of all points with pathways drawn by clinicians and the total risk scores of the pathways formed and followed by Q-learning. Artificial intelligence-based approaches may significantly contribute to the success of the surgical approaches examined. Furthermore, artificial intelligence can contribute to clinical outcomes in both preoperative surgical planning and intraoperative technical equipment-assisted neurosurgery. However, further studies with more detailed data are needed for more sensitive results.
颞叶内侧基底部区域相对复杂的功能解剖结构使得针对该区域的手术入路具有挑战性。多项研究描述了各种手术入路及其组合和改良方法，以到达该区域的病变部位。其中一些手术入路已通过基于人工智能的方法进行比较，这些方法可以对复杂数据进行预测、分类和分析。选择了几种手术入路进行比较，如经外侧裂前入路、经颞上沟入路、经颞中回入路、颞下 - 海马旁入路、乙状窦前 - 迷路后入路、幕上 - 枕下入路和小脑上蚓部 - 幕下入路。根据放射科指定的标准进行磁共振成像（MRI）检查。使用开源软件工具对头颅MRI的体积数据进行分割，并重建主要区域的解剖结构。采用Q学习算法来寻找与这些标准手术路径相似的路径。所选路径的Q学习分数如下：经外侧裂前入路（Q_A）= 31.01，经颞上沟入路（Q_B）= 25.00，经颞中回入路（Q_C）= 28.92，颞下 - 海马旁入路（Q_D）= 23.51，乙状窦前 - 迷路后入路（Q_E）= 27.54，幕上 - 枕下入路（Q_F）= 27.2，以及小脑上蚓部 - 幕下入路（Q_G）= 21.04。在所有检查的入路中，小脑上蚓部 - 幕下入路的Q值分数最高，因此是最佳的。还发现临床医生绘制的路径上所有点的总风险分数与Q学习形成并遵循的路径的总风险分数之间存在差异。基于人工智能的方法可能会显著促进所检查手术入路的成功。此外，人工智能在术前手术规划和术中技术设备辅助神经外科手术中都有助于改善临床结果。然而，需要更详细的数据进行进一步研究以获得更敏感的结果。
