Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making

Samareh, Aven; Chang, Xiangyu; Lober, William B.; Evans, Heather L.; Wang, Zhangyang; Qian, Xiaoning; Huang, Shuai

doi:10.1089/sur.2019.150

Cited by 11 publications

(9 citation statements)

References 52 publications

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“…When comparing the values of the individual biomarkers to clinical wound scores, the results of the present study demonstrate that digital signals can indicate the presence of an MSSA infection 24 to 31 h before standard methods. The ability to detect changes in tissue physiology associated with SSI is of critical importance to healthcare providers, as it would help enable the timely and effective treatment (e.g., antibiotics) to accelerate the recovery of patients ( 4 , 20 ), that would ultimately reduce healthcare costs, SSI-associated complications and mortality rates.…”

Section: Discussionmentioning

confidence: 99%

“…With increasing demand for healthcare, SSIs need to be predicted and diagnosed early so that timely and effective treatment (e.g., antibiotics) can be implemented to accelerate the recovery of patients ( 4 , 18 – 20 ). Fortunately, normal surgical wound healing processes occur in a defined and organized fashion, requiring oxygen to promote constructive healing and fight off any contaminants or organisms ( 21 ).…”

Section: Introductionmentioning

confidence: 99%

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The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Hughes

Jeffers²,

Sethuraman³

et al. 2023

Front. Med. Technol.

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IntroductionSurgical Site Infection (SSI) is a common healthcare-associated infection that imposes a considerable clinical and economic burden on healthcare systems. Advances in wearable sensors and digital technologies have unlocked the potential for the early detection and diagnosis of SSI, which can help reduce this healthcare burden and lower SSI-associated mortality rates.MethodsIn this study, we evaluated the ability of a multi-modal bio-signal system to predict current and developing superficial incisional infection in a porcine model infected with Methicillin Susceptible Staphylococcus Aureus (MSSA) using a bagged, stacked, and balanced ensemble logistic regression machine learning model.ResultsResults demonstrated that the expression levels of individual biomarkers (i.e., peri-wound tissue oxygen saturation, temperature, and bioimpedance) differed between non-infected and infected wounds across the study period, with cross-correlation analysis indicating that a change in bio-signal expression occurred 24 to 31 hours before this change was reflected by clinical wound scoring methods employed by trained veterinarians. Moreover, the multi-modal ensemble model indicated acceptable discriminability to detect the presence of a current superficial incisional SSI (AUC = 0.77), to predict an SSI 24 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.80), and to predict an SSI 48 hours in advance of veterinarian-based SSI diagnosis (AUC = 0.74).DiscussionIn sum, the results of the current study indicate that non-invasive multi-modal sensor and signal analysis systems have the potential to detect and predict superficial incisional SSIs in porcine subjects under experimental conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Hughes

Jeffers²,

Sethuraman³

et al. 2023

Front. Med. Technol.

View full text Add to dashboard Cite

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“…Finally, purely data-driven approaches on unstructured data are becoming ubiquitous thanks to the development of advanced machine learning techniques [27], [28], [29]. Extracting information from text using natural language processing algorithms is especially difficult as the models need to capture different semantics within the words [30].…”

Section: A Related Workmentioning

confidence: 99%

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections

Boubekki

Myhre

Luppino

et al. 2022

IEEE J. Biomed. Health Inform.

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Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who underwent gastrointestinal surgery and developed either deep, shallow or no infection. We represent the patients using the concentrations of fourteen common blood components collected over the four weeks preceding the surgery partitioned into six time windows. A gradient boosting based classifier trained on our new set of features reports an AUROC of 0.991 for predicting a postoperative infection and and AUROC of 0.937 for classifying the severity of the infection. Further analyses support the clinical relevance of our approach as the most important features describe the nutritional status and the liver function over the two weeks prior to surgery.

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“…By using deep learning, the AI algorithm can also selfdeduct based on the abundant experiments from clinical surgeons and reconstruct clinical digitized data by uploading the surgical program to an AI surgical system to assist the surgery intelligently, including surgical excision range formulation, postoperative organ residual volume insurance, and prediction of lymph nodes with possibly positive metastasis [60] . The surgical planning and methods not only rely on surgeons, but they also depend on the program using the intelligent algorithm [61] . Of course, in the current stage, although AI surgical systems have achieved partial intelligence, they also need human supervision to a certain extent.…”

Section: Ai In Surgerymentioning

confidence: 99%

Application of Artificial Intelligence in Medicine: An Overview

et al. 2021

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Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

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Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making

Cited by 11 publications

References 52 publications

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model

Clinically Relevant Features for Predicting the Severity of Surgical Site Infections

Application of Artificial Intelligence in Medicine: An Overview

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