A new surgical path planning framework for neurosurgery.

BACKGROUND: Despite 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. AIMS: The 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 & METHODS: The 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. RESULTS: This 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. DISCUSSION: In 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. CONCLUSION: This 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.

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

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.

Soil productivity analysis based on a fuzzy logic system.

BACKGROUND: Maintaining soil productivity is essential if agriculture production systems are to be sustainable. However, there is a paucity of tools for measurement for the purpose of understanding changes in soil productivity. Fuzzy logic-based analysis offers this possibility. It is a new method on the evaluation of soil productivity in Turkey and even in the world. RESULTS: Values for pH, salinity, carbonate and organic matter were entered into the system as input variables so as to obtain soil productivity as the output. After the membership functions related to input and output were determined, rules were created. Then, the fuzzy logic system was applied separately to pH, salinity, lime and organic matter values of different soil types present in the Kocaeli region with the aim of obtaining corresponding fuzzy values. Thus, soil productivity profiles of the region were deciphered. CONCLUSION: Organic matter levels in the study field remained below 30 g kg(-1) and varied between 22 and 28 g kg(-1). Productivity values were obtained as a percentage and varied between 16.9% and 18.1%. The lime content of the study soils varied in the range of 33-88 g kg(-1). Average totals for salt values of the field changed between 0.58 and 0.77 g kg(-1).

Prototype of a tool for analysing laryngeal cancer operations.

In this study, a software tool was developed to analyse the medical data collected from laryngeal cancer operations by using two data mining techniques. The software, run on real-world medical data, is a tool that enables medical decisions to be reached by analysing past records from patients. The k-means algorithm, which is a clustering algorithm in data mining, was used to point out the intensities in the data set and to display two dimensions on the charts. The data of three screens that were named as selective clustering, different pre- and post-operation stages and clustering operations based on pre-operation T values, were processed using clustering with the k-means algorithm and one screen, which named relapse and survival percentages, was processed through classifying. It helps the future decision-making process by considering false estimates of pre-operation stages of the cases and by using the information gathered from past cases concerning tumour relapse and the survival percentage for prognostication. The characteristics of laryngeal cancer operations data, that involve causal links, were exposed by using two data mining techniques in this application.