Publication: Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
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Date
2022-04-01T00:00:00Z
Authors
Dündar, Tolga Turan
Yurtsever, İsmail
Kurt Pehlivanoğlu, Meltem
Yıldız, Uğur
Eker, Ayşegül
Demir, Mehmet Ali
Mutluer, Ahmet Serdar
Tektaş, Recep
Kazan, Mevlude Sila
Kitiş, Serkan
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Abstract
Objectives: Artificial intelligence (AI) applications in neurosurgery have an increasing
momentum as well as the growing number of implementations in the medical literature.
In recent years, AI research define a link between neuroscience and AI. It is a connection
between knowing and understanding the brain and how to simulate the brain. The
machine learning algorithms, as a subset of AI, are able to learn with experiences,
perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches
that have been defined, disciplined, and developed in the last century have become
more effective with technological developments. We aimed to define individual-safe,
intracranial approaches by introducing functional anatomical structures and pathological
areas to artificial intelligence.
Methods: Preoperative MR images of patients with deeply located brain tumors were
used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered.
Voxel values of these selected regions in cranial MR sequences were extracted and
labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is
a model-free reinforcement learning algorithm was run on labeled voxel values (on
optimal paths extracted from the new heuristic-based path planning algorithm), then
the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the
maximum tumor tissue and in the meantime that functional anatomical tissues will be
least affected.
Results: The most suitable cranial entry areas were found with the artificial intelligence
algorithm. Cortico-tumoral pathways were revealed using Q-learning from these
optimal points.
Conclusions: AI will make a significant contribution to the positive outcomes as its use
in both preoperative surgical planning and intraoperative technique equipment assisted
neurosurgery, its use increased
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Keywords
Approaches, Artificial intelligence (AI), Brain tumor, Cranial approaches, Machine learning, Neurosurgery, Neurosurgical planning