Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer

Meta-lasso: new insight on infection prediction after minimally invasive surgery

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Machine learning approach to predict tracheal necrosis after total pharyngolaryngectomy

BACKGROUND

Tracheal necrosis is a potentially severe complication of total pharyngolarynjectomy (TPL), sometimes combined with total esophagectomy. The risk factors for tracheal necrosis after TPL without total esophagectomy remain unknown.

METHODS

We retrospectively reviewed data of 395 patients who underwent TPL without total esophagectomy. Relevant factors associated with tracheal necrosis were evaluated using random forest machine learning and traditional multivariable logistic regression models.

RESULTS

Tracheal necrosis occurred in 25 (6.3%) patients. Both the models identified almost the same factors relevant to tracheal necrosis. History of radiotherapy was the most important predicting and significant risk factor in both models. Paratracheal lymph node dissection and total thyroidectomy with TPL were also relevant. Random forest model was able to predict tracheal necrosis with an accuracy of 0.927.

CONCLUSIONS

Random forest is useful in predicting tracheal necrosis. Countermeasures should be considered when creating a tracheostoma, particularly in patients with identified risk factors.

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

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.

Artificial Intelligence Modeling to Predict Periprosthetic Infection and Explantation following Implant-Based Reconstruction

BACKGROUND

Despite improvements in prosthesis design and surgical techniques, periprosthetic infection and explantation rates following implant-based reconstruction (IBR) remain relatively high. Artificial intelligence is an extremely powerful predictive tool that involves machine learning (ML) algorithms. We sought to develop, validate, and evaluate the use of ML algorithms to predict complications of IBR.

METHODS

A comprehensive review of patients who underwent IBR from January of 2018 to December of 2019 was conducted. Nine supervised ML algorithms were developed to predict periprosthetic infection and explantation. Patient data were randomly divided into training (80%) and testing (20%) sets.

RESULTS

The authors identified 481 patients (694 reconstructions) with a mean ± SD age of 50.0 ± 11.5 years, mean ± SD body mass index of 26.7 ± 4.8 kg/m 2 , and median follow-up time of 16.1 months (range, 11.9 to 3.2 months). Periprosthetic infection developed in 113 of the reconstructions (16.3%), and explantation was required with 82 (11.8%) of them. ML demonstrated good discriminatory performance in predicting periprosthetic infection and explantation (area under the receiver operating characteristic curve, 0.73 and 0.78, respectively), and identified nine and 12 significant predictors of periprosthetic infection and explantation, respectively.

CONCLUSIONS

ML algorithms trained using readily available perioperative clinical data accurately predict periprosthetic infection and explantation following IBR. The authors' findings support incorporating ML models into perioperative assessment of patients undergoing IBR to provide data-driven, patient-specific risk assessment to aid individualized patient counseling, shared decision-making, and presurgical optimization.

Development of a knowledge-based healthcare-associated infections surveillance system in China

BACKGROUND

In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations.

METHODS

We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts.

RESULTS

We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability.

CONCLUSIONS

Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

A History of Innovation: Tracing the Evolution of Imaging Modalities for the Preoperative Planning of Microsurgical Breast Reconstruction

Breast reconstruction is an essential component in the multidisciplinary management of breast cancer patients. Over the years, preoperative planning has played a pivotal role in assisting surgeons in planning operative decisions prior to the day of surgery. The evolution of preoperative planning can be traced back to the introduction of modalities such as ultrasound and colour duplex ultrasonography, enabling surgeons to evaluate the donor site's vasculature and thereby plan operations more accurately. However, the limitations of these techniques paved the way for the implementation of modern three-dimensional imaging technologies. With the advancements in 3D imaging, including computed tomography and magnetic resonance imaging, surgeons gained the ability to obtain detailed anatomical information. Moreover, numerous adjuncts have been developed to aid in the planning process. The integration of 3D-printing technologies has made significant contributions, enabling surgeons to create complex haptic models of the underlying anatomy. Direct infrared thermography provides a non-invasive, visual assessment of abdominal wall vascular physiology. Additionally, augmented reality technologies are poised to reshape surgical planning by providing an immersive and interactive environment for surgeons to visualize and manipulate 3D reconstructions. Still, the future of preoperative planning in breast reconstruction holds immense promise. Most recently, artificial intelligence algorithms, utilising machine learning and deep learning techniques, have the potential to automate and enhance preoperative planning processes. This review provides a comprehensive assessment of the history of innovation in preoperative planning for breast reconstruction, while also outlining key future directions, and the impact of artificial intelligence in this field.

Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap Necrosis

OBJECTIVE

To develop, validate, and evaluate ML algorithms for predicting MSFN.

BACKGROUND

MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy.

METHODS

We conducted comprehensive review of all consecutive patients who underwent mastectomy and immediate implant-based reconstruction from January 2018 to December 2019. Nine supervised ML algorithms were developed to predict MSFN. Patient data were partitioned into training (80%) and testing (20%) sets.

RESULTS

We identified 694 mastectomies with immediate implant-based reconstruction in 481 patients. The patients had a mean age of 50 ± 11.5 years, years, a mean body mass index of 26.7 ± 4.8 kg/m 2 , and a median follow-up time of 16.1 (range, 11.9-23.2) months. MSFN developed in 6% (n = 40) of patients. The random forest model demonstrated the best discriminatory performance (area under curve, 0.70), achieved a mean accuracy of 89% (95% confidence interval, 83-94), and identified 10 predictors of MSFN. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. Higher body mass index, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and no acellular dermal matrix use were all independently associated with a higher risk of MSFN.

CONCLUSIONS

ML algorithms trained on readily available perioperative clinical data can accurately predict the occurrence of MSFN and aid in individualized patient counseling, preoperative optimization, and surgical planning to reduce the risk of this devastating complication.

Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections

Background. Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have different risk factors when compared to superficial SSIs. Machine learning (ML) algorithms provide the opportunity to analyze multiple factors to predict of the type and time of development of SSI. Therefore, we developed a ML model to predict type and postoperative week of SSI. Methodology. A case-control study was conducted among patients who developed a SSI after undergoing general surgery procedures at a tertiary care hospital between 2019 to 2020. Patients were followed for 30 days. Six ML algorithms were trained as predictors of type of infection (superficial vs deep/organ space) and time of infection, and tested using area under the receiver operating characteristic curve (AUC-ROC). Results. Data for 113 patients with SSIs was available. Of these 62 (54.8%) had superficial and 51 had (45.2%) deep/organ space infections. Compared with other ML algorithms, the XG boost univariate model had highest AUC-ROC (.84) for prediction of type of SSI and Stochastic gradient boosting univariate, logistic regression univariate, XG boost univariate, and random forest classification univariate model had the highest AUC-ROC (.74) for prediction of week of infection. Conclusions. ML models offer reasonable accuracy in prediction of superficial vs deep SSI and time of developing infection. Follow-up duration and allocation of treatment strategies can be informed by ML predictions.

Understanding the Impact of Aging on Attractiveness Using a Machine Learning Model of Facial Age Progression

Background: Advances in machine learning age progression technology offer the unique opportunity to better understand the public's perception on the aging face. Objective: To compare how observers perceive attractiveness and traditional gender traits in faces created with a machine learning model. Methods: Eight surveys were developed, each with 10 sets of photographs that were progressively aged with a machine learning model. Respondents rated attractiveness and masculinity or femininity of each photograph using a sliding scale (range: 0-100). Mean attractiveness scores were calculated and compared between men and women as well as between age groups. Results: A total of 315 respondents (51% men, 49% women) completed the survey. Accuracy of the facial age progression model was 85%. Females were considered significantly less attractive (-10.43, p < 0.01) and less feminine (-7.59, p < 0.01) per decade with the greatest drop over age 40 years. Male attractiveness and masculinity were relatively preserved until age 50 years where attractiveness scores were significantly lower (-5.45, p = 0.39). Conclusions: In this study, observers were found to perceive attractiveness at older ages differently between men and women.

The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction

BACKGROUND

Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction.

METHODS

We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss.

RESULTS

We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50).

CONCLUSIONS

ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.

A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models

Artificial intelligence (AI) focuses on processing and interpreting complex information as well as identifying relationships and patterns among complex data. Artificial intelligence- and machine learning (ML)-driven predictions have shown promising potential in influencing real-time decisions and improving surgical outcomes by facilitating screening, diagnosis, risk assessment, preoperative planning, and shared decision-making. Fundamental understanding of the algorithms, as well as their development and interpretation, is essential for the evolution of AI in surgery. In this article, we provide surgeons with a fundamental understanding of AI-driven predictive models through an overview of common ML and deep learning algorithms, model development, performance metrics and interpretation. This would serve as a basis for understanding ML-based research, while fostering new ideas and innovations for furthering the reach of this emerging discipline.

Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications

Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.

A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

Artificial intelligence (AI) is presently employed in several medical specialties, particularly those that rely on large quantities of standardized data. The integration of AI in surgical subspecialties is under preclinical investigation but is yet to be widely implemented. Plastic surgeons collect standardized data in various settings and could benefit from AI. This systematic review investigates the current clinical applications of AI in plastic and reconstructive surgery.

METHODS

A comprehensive literature search of the Medline, EMBASE, Cochrane, and PubMed databases was conducted for AI studies with multiple search terms. Articles that progressed beyond the title and abstract screening were then subcategorized based on the plastic surgery subspecialty and AI application.

RESULTS

The systematic search yielded a total of 1820 articles. Forty-four studies met inclusion criteria warranting further analysis. Subcategorization of articles by plastic surgery subspecialties revealed that most studies fell into aesthetic and breast surgery (27%), craniofacial surgery (23%), or microsurgery (14%). Analysis of the research study phase of included articles indicated that the current research is primarily in phase 0 (discovery and invention; 43.2%), phase 1 (technical performance and safety; 27.3%), or phase 2 (efficacy, quality improvement, and algorithm performance in a medical setting; 27.3%). Only one study demonstrated translation to clinical practice.

CONCLUSIONS

The potential of AI to optimize clinical efficiency is being investigated in every subfield of plastic surgery, but much of the research to date remains in the preclinical status. Future implementation of AI into everyday clinical practice will require collaborative efforts.

Machine-Learning Models for Predicting Surgical Site Infections using Patient Pre-Operative Risk and Surgical Procedure Factors

BACKGROUND

Surgical site infections (SSIs) are a significant healthcare problem as they can cause increased medical costs and increased morbidity and mortality. Assessing a patient's pre-operative risk factors can improve risk stratification and help guide the surgical decision-making process. Previous efforts to use pre-operative risk factors to predict the occurrence of SSIs have relied upon traditional statistical modeling approaches. The aim of this paper is to develop and validate, using state-of-the-art machine learning (ML) approaches, classification models for the occurrence of SSI to improve upon previous models.

METHODS

In this work, using the American College of Surgeons' National Surgical Quality Improvement Program (ACS NSQIP) database, the performances (e.g., prediction accuracy) of seven different ML approaches (Logistic Regression (LR), Naïve Bayesian (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN)) were compared. The performance of these models was evaluated using the area under the curve, accuracy, precision, sensitivity, and F1-score metrics.

RESULTS

Overall, 2,882,526 surgical procedures were identified in the study for the SSI predictive models' development. The results indicate that the DNN model offers the best predictive performance with 10-fold compared to the other six approaches considered (area under the curve = 0.8518, accuracy = 0.8518, precision = 0.8517, sensitivity = 0.8527, F1-score = 0.8518). Emergency case surgeries, American Society of Anesthesiologists (ASA) Index of 4 (ASA_4), BMI, Vascular surgeries, and general surgeries were most significant influencing features towards developing an SSI.

CONCLUSION

Equally important is that the commonly used LR approach for SSI prediction displayed mediocre performance. The results are encouraging as they suggest that the prediction performance for SSIs can be improved using modern ML approaches.

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review

Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field of surgery. This article aims to provide a comprehensive literature review of artificial intelligence as it applies to surgery and discuss practical examples, current applications, and challenges to the adoption of this technology. Furthermore, we elaborate on the utility of natural language processing and computer vision in improving surgical outcomes, research, and patient care.

Which criteria is a better predictor of ICU admission in trauma patients? An artificial neural network approach

PURPOSE

One of the most critical concerns in the intensive care unit (ICU) section is identifying the best criteria for entering patients to this part. This study aimed to predict the best compatible criteria for entering trauma patients in the ICU section.

METHOD

The present study was a historical cohort study. The data were collected from 2448 trauma patients referring to Shahid Rajaee Hospital between January 2015 and January 2017 in Shiraz, Iran. The artificial neural network (ANN) models with cross-validation and logistic regression (LR) with a backward method was used for data analysis. The final analysis was performed on a total of 958 patients who were transferred to the ICU section.

RESULTS

Based on the present results, the motor component of the GCS score at each cutoff point had the highest importance. The results also showed better performance for the AUC and accuracy rate for ANN compared with LR.

CONCLUSION

The most critical indicators in predicting the optimal use of ICU services in this study were the Motor component of the GCS. Results revealed that the ANN had a better performance than the LR in predicting the main outcomes of the traumatic patients in both the accuracy and AUC index. Trauma section surgeons and ICU specialists will benefit from this study's results and can assist them in making decisions to predict the patient outcomes before entering the ICU.

Machine Learning and Artificial Intelligence for Surgical Decision Making

Background: The use of machine learning (ML) and artificial intelligence (AI) in medical research continues to grow as the amount and availability of clinical data expands. These techniques allow complex interpretation of data and capture non-linear relations not immediately apparent by classic statistical techniques. Methods: This review of the ML/AI literature provides a brief overview for practicing surgeons and clinicians of the current and future roles these methods will have within surgical infection research. Results: A conceptual overview of the techniques is provided along with concrete examples in the surgical infections literature. Further examples of ML/AI techniques in clinical decision support as well as therapy discovery with model-based deep reinforcement learning are illustrated. Conclusions: Artificial intelligence and ML are important and increasingly utilized techniques within the expanding body of surgical infection research. This article provides a minimal baseline literacy in ML/AI to be able to view such projects in an appropriately critical fashion.

Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery

INTRODUCTION

Machine learning (ML) is a set of models and methods that can detect patterns in vast amounts of data and use this information to perform various kinds of decision-making under uncertain conditions. This review explores the current role of this technology in plastic surgery by outlining the applications in clinical practice, diagnostic and prognostic accuracies, and proposed future direction for clinical applications and research.

METHODS

EMBASE, MEDLINE, CENTRAL and ClinicalTrials.gov were searched from 1990 to 2020. Any clinical studies (including case reports) which present the diagnostic and prognostic accuracies of machine learning models in the clinical setting of plastic surgery were included. Data collected were clinical indication, model utilised, reported accuracies, and comparison with clinical evaluation.

RESULTS

The database identified 1181 articles, of which 51 articles were included in this review. The clinical utility of these algorithms was to assist clinicians in diagnosis prediction (n=22), outcome prediction (n=21) and pre-operative planning (n=8). The mean accuracy is 88.80%, 86.11% and 80.28% respectively. The most commonly used models were neural networks (n=31), support vector machines (n=13), decision trees/random forests (n=10) and logistic regression (n=9).

CONCLUSIONS

ML has demonstrated high accuracies in diagnosis and prognostication of burn patients, congenital or acquired facial deformities, and in cosmetic surgery. There are no studies comparing ML to clinician's performance. Future research can be enhanced using larger datasets or utilising data augmentation, employing novel deep learning models, and applying these to other subspecialties of plastic surgery.

Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence

OBJECTIVES

The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular aneurysm repair (EVAR) by artificial intelligence for more accurate surveillance in practice.

METHODS

A single-centre prospective data collection on (n = 250) EVAR cases with n = 26 preoperative attributes (factors) on endpoint of endoleak (types I-VI), occlusion, migration and mortality over a 13-year period was conducted. In addition to the traditional statistical analysis, data was subjected to machine learning algorithm through artificial neural network. The predictive accuracy (specificity and -1 sensitivity) on each endpoint is presented with percentage and receiver operative curve. The pattern recognition and model classification were conducted using discriminate analysis, decision tree, logistic regression, naive Bayes and support vector machines, and the best fit model was deployed for pattern recognition and modelling.

RESULTS

The accuracy of the training, validation and predictive ability of artificial neural network in detection of endoleak type I was 95, 96 and 94%, type II (94, 83, 90 and 82%) and type III was 96, 94 and 96%, respectively. Endpoints are associated with increase in weights through predictive modeling that were not detected through statistical analytics. The overall accuracy of the model was >86%.

CONCLUSION

The study highlights the applicability, accuracy and reliability of artificial intelligence in the detection of adverse outcomes post-EVAR for an accurate surveillance stratification.

The applications of machine learning in plastic and reconstructive surgery: protocol of a systematic review

BACKGROUND

Machine learning, a subset of artificial intelligence, is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information and use it to perform various kinds of decision-making under uncertain conditions. This can assist surgeons in clinical decision-making by identifying patient cohorts that will benefit from surgery prior to treatment. The aim of this review is to evaluate the applications of machine learning in plastic and reconstructive surgery.

METHODS

A literature review will be undertaken of EMBASE, MEDLINE and CENTRAL (1990 up to September 2019) to identify studies relevant for the review. Studies in which machine learning has been employed in the clinical setting of plastic surgery will be included. Primary outcomes will be the evaluation of the accuracy of machine learning models in predicting a clinical diagnosis and post-surgical outcomes. Secondary outcomes will include a cost analysis of those models. This protocol has been prepared using the Preferred Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.

DISCUSSION

This will be the first systematic review in available literature that summarises the published work on the applications of machine learning in plastic surgery. Our findings will provide the basis of future research in developing artificial intelligence interventions in the specialty.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42019140924.

Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies

BACKGROUND

Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work.

OBJECTIVES

To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management.

SOURCES

A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014-2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included.

CONTENT

Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking.

IMPLICATIONS

Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed.

Trends and predictors of mortality in unstable pelvic ring fracture: a 10-year experience with a multidisciplinary institutional protocol

Background

Pelvic ring fracture is often combined with other injuries and such patients are considered at high risk of mortality and complications. There is controversy regarding the gold standard protocol for the initial treatment of pelvic fracture. The aim of this study was to assess which risk factors could affect the outcome and to analyze survival using our multidisciplinary institutional protocol for traumatic pelvic ring fracture.

Material and methods

This retrospective study reviewed patients who sustained an unstable pelvic ring fracture with Injury Severity Score (ISS) ≥ 5. All patients were admitted to the emergency department and registered in the Trauma Registry System of a level I trauma center from January 1, 2008, to December 31, 2017. The annular mortality rate after the application of our institutional protocol was analyzed. Patients with different systems of injury and treatments were compared, and regression analysis was performed to adjust for factors that could affect the rate of mortality and complications.

Results

During the 10-year study period, there were 825 unstable pelvic ring injuries, with a mean ISS higher than that of other non-pelvic trauma cases. The annual mortality rate declined from 7.8 to 2.4% and the mean length of stay was 18.1 days. A multivariable analysis showed that unstable initial vital signs, such as systolic blood pressure < 90 mmHg (odds ratio [OR] 2.53; confidence interval [CI] 1.11–5.73), Glasgow Coma Scale < 9 (OR 3.87; CI 1.57–9.58), 24 > ISS > 15 (OR 4.84; CI 0.85–27.65), pulse rate < 50 (OR 11.54; CI 1.21–109.6), and diabetes mellitus (OR 3.18; CI 1.10–9.21) were associated with higher mortality. No other specific system in the high Abbreviated Injury Scale increased the rates of mortality or complications.

Conclusion

Poor initial vital signs and Glasgow Coma Scale score, higher ISS score, and comorbidity of diabetes mellitus affect the mortality rate of patients with unstable pelvic ring fractures. No single system of injury was found to increase mortality in these patients. The mortality rate was reduced through institutional efforts toward the application of guidelines for the initial management of pelvic fracture.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

The Role of Artificial Intelligence in the Prediction of Functional Maturation of Arteriovenous Fistula

Objective: The aim of this study is to examine the application of virtual artificial intelligence (AI) in the prediction of functional maturation (FM) and pattern recognition of factors in autogenous radiocephalic arteriovenous fistula (RCAVF) formation. Materials and Methods: A prospective database of 266 individuals over a four-year period with n=10 variables were used to train, validate and test an artificial neural network (ANN). The ANN was constructed to create a predictive model and evaluate the impact of variables on the endpoint of FM. Results: The overall accuracy of the training, validation, testing and all data on each output matrix at detecting FM was 86.4%, 82.5%, 77.5% and 84.5%, respectively. The results corresponded with their area under the curve for each output matrix at best sensitivity and at 1-specificity with the log-rank test p<0.01. ANN classification identified age, artery and vein diameter to influence FM with an accuracy of (>89%). AI has the ability of predicting with a high grade of accuracy FM and recognising patterns that influence it. Conclusion: AI is a replicable tool that could remain up to date and flexible to ongoing deep learning with further data feed ensuring substantial enhancement in its accuracy. AI could serve as a clinical decision-making tool and its application in vascular access requires further evaluation.

Introduction to Machine Learning in Digital Healthcare Epidemiology

To exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists and data analysts, healthcare epidemiologists should have some knowledge of large-scale analysis techniques, particularly about machine learning. This review focuses on the broad area of machine learning and its first applications in the emerging field of digital healthcare epidemiology.