Machine Learning and Artificial Intelligence in Surgical Research

Srinivas, Shruthi; Young, Andrew J.

doi:10.1016/j.suc.2022.11.002

Cited by 3 publications

(16 citation statements)

References 88 publications

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“…Plastic surgery, a specialized medical branch, has also seen the integration of machine learning techniques for various purposes. Below are some areas where machine learning is being used in plastic surgery [1][2][3].…”

Section: Editorialmentioning

confidence: 99%

“…This aids surgeons in preoperative assessment and surgical planning [3,4]. This also allows for more precise and personalized surgical planning [1][2][3][4][5]. Machine learning algorithms can automatically segment different tissues in medical images.…”

Section: Editorialmentioning

confidence: 99%

“…In plastic surgery, this can be useful for accurately delineating anatomical structures like skin, muscles, and bones. Automated tissue segmentation can save time and improve the accuracy of surgical planning [1][2][3][4][5].…”

Section: Editorialmentioning

confidence: 99%

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Precision and Progress: Machine Learning Advancements in Plastic Surgery

Mir¹,

Maurya²

2023

Cureus

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Machine learning has emerged as a powerful tool in various healthcare domains, and its application in plastic surgery has shown significant promise. Plastic surgery aims to enhance and reconstruct physical appearance and function, making it an ideal field for integrating machine learning techniques. This abstract presents an overview of the applications, challenges, and potential benefits of machine learning in plastic surgery.One of the key areas where machine learning has been applied is in the preoperative assessment and surgical planning process. By analyzing large datasets of patient images and clinical data, machine learning algorithms can assist plastic surgeons in predicting surgical outcomes, identifying optimal surgical techniques, and minimizing potential complications. These algorithms can learn from past cases and provide valuable insights to improve surgical decision-making and optimize patient care.Furthermore, machine learning has shown promise in facial recognition and analysis, which is crucial in plastic surgery procedures involving the face. Algorithms can accurately detect facial landmarks, assess facial symmetry, and simulate potential surgical outcomes. This technology gives plastic surgeons a more comprehensive understanding of the patient's facial structure and aids in designing personalized treatment plans.Additionally, machine learning algorithms have been employed to automate the analysis of large-scale clinical databases, assisting in identifying patterns, risk factors, and treatment outcomes. By leveraging these algorithms, plastic surgeons can gain valuable insights into patient populations, surgical trends, and postoperative complications. This information can inform clinical decision-making, improve patient safety, and enhance the overall quality of care.Despite the numerous advantages, several challenges need to be addressed when integrating machine learning into plastic surgery. These include the need for high-quality and diverse datasets, algorithm interpretability, ethical considerations, and regulatory compliance.

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Section: Editorialmentioning

confidence: 99%

Section: Editorialmentioning

confidence: 99%

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Precision and Progress: Machine Learning Advancements in Plastic Surgery

Mir¹,

Maurya²

2023

Cureus

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“…Artificial Intelligence (AI) and Machine learning (ML) represent an important evolution in computer science and data processing systems which can be used in order to improve almost every technology-enabled service, product, and industrial application (Soori et al, 2023). More specifically, ML is a branch of artificial intelligence involving teaching a computer to read and interpret data (Srinivas & Young, 2023), and it is a subfield of artificial intelligence and computer science. It focuses on using data and algorithms to simulate the learning process of machines and enhance the accuracy of the systems (Magar & Farimani, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Over the past years, studies on ML and the combination of different ML methods have become a trend (Ren et al, 2022), and a growing body of research is applying ML in several areas, such as business -considering ML a tool that can be used in cryptocurrency research (Ren et al, 2022), for stock market prediction (Mintarya et al, 2023); in health and medical research, there are studies for prediction in medical and surgical research (Srinivas & Young, 2023), to identify the predictors of smoking (Bickel et al, 2023); ML based methodologies have also accelerated the prediction of the physical properties of materials (Magar & Farimani, 2023), and many others. However, ML is still experimental because no universal learning algorithm exists, even though the number of ML algorithms is extensive and growing (Liu et al, 2021;Nguyen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing

De Oliveira Santos,

Schneider Hahn

2023

Multidiscip. bus. rev (Impr.)

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The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.

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Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

Feng,

Wang,

Liu

et al. 2024

Sci Rep

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Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

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Machine Learning and Artificial Intelligence in Surgical Research

Cited by 3 publications

References 88 publications

Precision and Progress: Machine Learning Advancements in Plastic Surgery

Precision and Progress: Machine Learning Advancements in Plastic Surgery

A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing

Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study

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