Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium

Dündar, Tolga Turan; Yurtsever, İsmail; Pehlıvanoğlu, Meltem Kurt; YILDIZ, Uğur; Eker, Aysegul; Demir, Mehmet Ali; Mutluer, Ahmet Serdar; Tektaş, Recep; Kazan, Mevlude Sila; Kitiş, Serkan; Gökoğlu, Abdülkerim; Doğan, İhsan; Duru, N. Jeremi

doi:10.3389/fsurg.2022.863633

Cited by 34 publications

(12 citation statements)

References 26 publications

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“…Recent advances in the field of machine learning have improved the discovery of new biomarkers for disease diagnosis or helped in designing treatment plans [ 14 , 25 , 26 , 27 ]. Dundar et al [ 28 ] utilized a proposed machine-learning surgical planning and found that it significantly contributed to positive outcomes for neurosurgery. Sakatani et al [ 29 ] utilized a machine-learning approach to estimate human cerebral atrophy on the basis of metabolic status.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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The rising incidence of fatty liver disease (FLD) poses a health challenge, and is expected to be the leading global cause of liver-related morbidity and mortality in the near future. Early case identification is crucial for disease intervention. A retrospective cross-sectional study was performed on 31,930 Taiwanese subjects (25,544 training and 6386 testing sets) who had received health check-ups and abdominal ultrasounds in Changhua Christian Hospital from January 2009 to January 2019. Clinical and laboratory factors were included for analysis by different machine-learning algorithms. In addition, the performance of the machine-learning algorithms was compared with that of the fatty liver index (FLI). Totally, 6658/25,544 (26.1%) and 1647/6386 (25.8%) subjects had moderate-to-severe liver disease in the training and testing sets, respectively. Five machine-learning models were examined and demonstrated exemplary performance in predicting FLD. Among these models, the xgBoost model revealed the highest area under the receiver operating characteristic (AUROC) (0.882), accuracy (0.833), F1 score (0.829), sensitivity (0.833), and specificity (0.683) compared with those of neural network, logistic regression, random forest, and support vector machine-learning models. The xgBoost, neural network, and logistic regression models had a significantly higher AUROC than that of FLI. Body mass index was the most important feature to predict FLD according to the feature ranking scores. The xgBoost model had the best overall prediction ability for diagnosing FLD in our study. Machine-learning algorithms provide considerable benefits for screening candidates with FLD.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Chen

Lin

Yen

et al. 2022

JPM

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“…They demonstrate mathematically how this method allows the surgeon to access parts of the brain that a straight cannula, the present state of the art, would not allow. In 2022, Dundar et al 25 introduced a heuristic-based surgical path planning algorithm integrated with Q-learning, a popular AI paradigm for reinforcement learning, to discover the right skull entrance points, nonlinear and optimal linear pathways to assure minimally invasive tumour excision.…”

Section: Previous Surgical Path Planning Methods For Neurosurgerymentioning

confidence: 99%

“…In 2022, Dundar et al. 25 introduced a heuristic‐based surgical path planning algorithm integrated with Q‐learning, a popular AI paradigm for reinforcement learning, to discover the right skull entrance points, nonlinear and optimal linear pathways to assure minimally invasive tumour excision.…”

Section: Introductionmentioning

confidence: 99%

A new surgical path planning framework for neurosurgery

Kurt Pehlivanoğlu,

Ay,

Eker

et al. 2023

Robotics Computer Surgery

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BackgroundDespite using a variety of path‐finding algorithms that use tracts, the most significant advancement in this study is considering the values of all brain areas by doing atlas‐based segmentation for a more precise search. Our motivation comes from the literature’s shortcomings in designing and implementing path‐planning methods. Since planning paths with curvatures is a complex problem that requires considering many surgical and physiological constraints, most path‐planning strategies focus on straight paths. There is also a lack of studies that focus on the complete structure of the brain with the tracks, veins, and segmented areas. Instrument dependence is another inadequacy of the methods proposed in the literature.AimsThe aim of this study is to design a new surgical path planning framework that helps to plan the surgical path independently of the instrument, considers the entire structure of the brain, and allows curvilinear surgical paths. Thus, neurosurgeons can generate patient‐specific possible optimal surgical pathways before the neurosurgical procedure.Materials & MethodsThe proposed framework includes different path‐finding algorithms (Dijkstra, A*, and their aggressive variants) that find optimal paths by taking the risk scores (surgeons assessed all the segmented regions, considering the extent of damage. In this evaluation, scores ranged from “0 to 10,” with the most critical areas receiving a score of “10,” while the least possible affected areas were assigned a score of "0") for sensitive brain areas into consideration. For the tract image processing the framework includes fractional anisotropy (FA), relative anisotropy (RA), spherical measure (SM), and linear measure (LM) methods.ResultsThis is the first paper to handle tracts and atlas‐based segmentation of the human brain altogether under a framework for surgical path planning. The framework has a dynamic structure that gives the flexibility to add different path‐finding algorithms and generate different widths of surgical pathways. Moreover, surgeons can update the score table to guarantee minimally invasive surgery. The output file format of all the extracted surgical paths is NRRD, so it can be easily visualised, analysed, or processed over the third part software tools.DiscussionIn this study, we generated many possible surgical pathways then these pathways were evaluated by the surgeons the results were impressive because the framework could identify surgical pathways used in real‐world surgery that correspond to the standard pathways such as anterior transsylvian, trans sulcal, transgyral, and sub‐temporal.ConclusionThis study proposes a new surgical path planning framework for neurosurgery. Moreover, in the future by adding/adopting different parameters (such as operation time, and short and long‐term complications after surgery) to the proposed framework, it would be possible to find new surgical pathways for difficult surgical conditions.

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“…Dundar et al proposed a heuristic-based surgical path planning algorithm combined with Q-learning, a frequently used reinforcement learning AI model, to identify the appropriate skull entry points, optimal linear, and nonlinear pathways to ensure minimally invasive approach for tumor resection. [16] Moreover, a better delineation of each patient's anatomy by CV can help orient the neurosurgeons for complex procedures such as trans-sphenoidal pituitary resection, malignant neoplasm excision, and skull-base surgeries. [57] Hand tremors during microsurgeries lead to increased angle resection, more collateral damage, and, hence, a higher rate of complications.…”

Section: Role Of Ai In Pre-and Post-operative Management In Neurosurgerymentioning

confidence: 99%

The future of artificial intelligence in neurosurgery: A narrative review

Iqbal

Jahangir

Mashkoor

et al. 2022

Surgical Neurology International

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Background: Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds. Methods: A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research. Results: The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models. Conclusion: Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

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Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium

Cited by 34 publications

References 26 publications

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population

A new surgical path planning framework for neurosurgery

The future of artificial intelligence in neurosurgery: A narrative review

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