Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

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Abstract

Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like ‘machine learning’, ‘surgical’ and ‘wound’. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn‐grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.

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Most cited references 36

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Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised 'associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.

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Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Quinlan Buchlak, Nazanin Esmaili, Jean-Christophe A Leveque … (2020)

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

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Author and article information

Contributors

Ramyar Farzan:

ORCID: https://orcid.org/0000-0002-7347-6574

ramyar.farzan2001@yahoo.com

Hamidreza Alizadeh Otaghvar:

ORCID: https://orcid.org/0000-0001-8535-3881

drhralizade@yahoo.com

Journal

Journal ID (nlm-ta): Int Wound J

Journal ID (iso-abbrev): Int Wound J

Journal ID (doi): 10.1111/(ISSN)1742-481X

Journal ID (publisher-id): IWJ

Title: International Wound Journal

Publisher: Blackwell Publishing Ltd (Oxford, UK )

ISSN (Print): 1742-4801

ISSN (Electronic): 1742-481X

Publication date (Electronic): 23 January 2024

Publication date Collection: January 2024

Volume: 21

Issue: 1 ( doiID: 10.1111/iwj.v21.1 )

Electronic Location Identifier: e14665

Affiliations

[ ¹ ] The Second Clinical Medical School Lanzhou University Lanzhou China

[ ² ] Department of Clinical Medicine, Health Science Center Lanzhou University Lanzhou China

[ ³ ] Department of Plastic & Reconstructive Surgery, School of Medicine Guilan University of Medical Sciences Rasht Iran

[ ⁴ ] Associate Professor of Plastic Surgery, Trauma and Injury Research Center Iran University of Medical Sciences Tehran Iran

Author notes

[*] [* ] Correspondence

Ramyar Farzan, Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.

Email: ramyar.farzan2001@ 123456yahoo.com

Hamidreza Alizadeh Otaghvar, Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran.

Email: drhralizade@ 123456yahoo.com

Author information

Ramyar Farzan https://orcid.org/0000-0002-7347-6574

Hamidreza Alizadeh Otaghvar https://orcid.org/0000-0001-8535-3881

Article

Publisher ID: IWJ14665

DOI: 10.1111/iwj.14665

PMC ID: 10805538

PubMed ID: 38272811

SO-VID: 7b0b4710-d126-4327-82a9-536034a07a86

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.