A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine

Sensor technology and machine learning to guide clinical decision making in plastic surgery

Subjective clinical evaluations are deeply rooted in medical practice. Recent advances in sensor technology facilitate the acquisition of extensive amounts of objective physiological data that can serve as a surrogate for subjective assessments. Along with sensor technology, a branch of artificial intelligence, known as machine learning, has provided decisive advances in several areas of medicine due to its pattern recognition and outcome prediction abilities. The assimilation of machine learning algorithms into sensor technology can substantially improve our current diagnostic and treatment competencies. This review explores available data on the use of sensor technology and machine learning in areas of interest for plastic surgeons, updates current knowledge on the most recent technological advances, and provides a new perspective on the field.

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 proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data

INTRODUCTION

Burn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score.

METHODS

Admission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test.

RESULTS

A total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72-0.94), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1) and 0.84 (95% CI = 0.74-0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75-0.95) and 0.84 (95% CI = 0.74-0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance.

CONCLUSION

This proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms.

LAY SUMMARY

Burn injuries are one of the most common traumatic injuries especially in countries with limited prevention and healthcare resources. To treat a patient with large burns who has been admitted to an intensive care unit, it is often necessary to assess the risk of a fatal outcome. Physicians traditionally use simplified scores to calculate risks. One commonly used score, the Baux score, uses age of the patient and the size of the burn to predict the risk of death. Adding the factor of inhalation injury, the score is then called the revised Baux score. However, there are a number of additional causes that can influence the risk of fatal outcomes that Baux scores do not take into account. Machine learning is a method of data modelling where the system learns to predict outcomes based on previous cases and is a branch of artificial intelligence. In this study we evaluated several machine learning methods for outcome prediction in patients admitted for burn injury. We gathered data on 93 patients at admission to the intensive care unit and our experiments show that machine learning methods can reach an accuracy comparable with Baux scores in calculating the risk of fatal outcomes. This study represents a proof of principle and future studies on larger patient series are required to verify our results as well as to evaluate the methods on patients in real-life situations.

Artificial intelligence in the management and treatment of burns: a systematic review

BACKGROUND

Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies.

METHODS

A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods.

RESULTS

A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively.

CONCLUSION

AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.

A systematic review of machine learning and automation in burn wound evaluation: A promising but developing frontier

BACKGROUND

Visual evaluation is the most common method of evaluating burn wounds. Its subjective nature can lead to inaccurate diagnoses and inappropriate burn center referrals. Machine learning may provide an objective solution. The objective of this study is to summarize the literature on ML in burn wound evaluation.

METHODS

A systematic review of articles published between January 2000 and January 2021 was performed using PubMed and MEDLINE (OVID). Articles reporting on ML or automation to evaluate burn wounds were included. Keywords included burns, machine/deep learning, artificial intelligence, burn classification technology, and mobile applications. Data were extracted on study design, method of data acquisition, machine learning techniques, and machine learning accuracy.

RESULTS

Thirty articles were included. Nine studies used machine learning and automation to estimate percent total body surface area (%TBSA) burned, 4 calculated fluid estimations, 19 estimated burn depth, 5 estimated need for surgery, and 2 evaluated scarring. Models calculating %TBSA burned demonstrated accuracies comparable to or better than paper methods. Burn depth classification models achieved accuracies of >83%.

CONCLUSION

Machine learning provides an objective adjunct that may improve diagnostic accuracy in evaluating burn wound severity. Existing models remain in the early stages with future studies needed to assess their clinical feasibility.

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.

Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data

Cerebral white matter lesions are ischemic symptoms caused mainly by microangiopathy; they are diagnosed by MRI because they show up as abnormalities in MRI images. Because patients with white matter lesions do not have any symptoms, MRI often detects the lesions for the first time. Generally, head MRI for the diagnosis and grading of cerebral white matter lesions is performed as an option during medical checkups in Japan. In this study, we develop a mathematical model for the prediction of white matter lesions using data from routine medical evaluations that do not include a head MRI. Linear discriminant analysis, logistic discrimination, Naive Bayes classifier, support vector machine, and random forest were investigated and evaluated by ten-fold cross-validation, using clinical data for 1,904 examinees (988 males and 916 females) from medical checkups that did include the head MRI. The logistic regression model was selected based on a comparison of accuracy and interpretability. The model variables consisted of age, gender, plaque score (PS), LDL, systolic blood pressure (SBP), and administration of antihypertensive medication (odds ratios: 2.99, 1.57, 1.18, 1.06, 1.12, and 1.52, respectively) and showed Areas Under the ROC Curve (AUC) 0.805, the model displayed sensitivity of 72.0%, and specificity 75.1% when the most appropriate cutoff value was used, 0.579 as given by the Youden Index. This model has shown to be useful to identify patients with a high-risk of cerebral white matter lesions, who can then be diagnosed with a head MRI examination in order to prevent dementia, cerebral infarction, and stroke.

Epidemiological analysis of 9,779 burn patients in China: An eight-year retrospective study at a major burn center in southwest China

Burns are tissue injuries caused by high temperature, chemicals or electricity. Severe burns may involve all of the organs and tissues of the human body, leading to a series of pathophysiological processes and even death. The present study reviewed the clinical data of burn patients, including cases of burn-associated death, to provide evidence for the strategy of burn prevention. Basic information from 13,205 inpatients treated between January 1, 2009 and December 31, 2016 was extracted from the database of the Institute of Burn Research at Southwest Hospital (Chongqing, China). After excluding 3,426 inpatients who were not primarily treated for burns, 9,779 patients remained; among them, 68 cases (0.7%) had died as a direct consequence of the burns. Based on baseline data, the mortality rate, total body surface area of the burn (TBSA), age, sex, cause of injury and complications were analysed. In general, males accounted for a higher percentage than female burn patients. Of the patients, 95.54% had a TBSA of <50%, and the rate of mortality of the patients was increased when the TBSA was ≥50%. The major causes of injury were scalds (41.60%), fire (26.92%) and electricity (15.29%), and the majority of victims were 14 years or younger. With improvements in burn treatment technology in recent years, burn patient mortality was significantly reduced. Complications, including multiple organ failure and severe systemic infection, may reduce the survival rate of patients. The major risk factors for death included burns resulting from explosions, as well as shock, age (aged 0-1 or ≥50 years), greater TBSA and full-thickness burn area. With increasing length of stay at the hospital, patient mortality decreased. The renewal of treatment concepts and refined patient management contributed to the shorter LOS and lower mortality in 2015 and 2016.

A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema

Purpose: Artificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema. Methods: Patient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables. Three groups were randomly devised to build, test and demonstrate the accuracy of the algorithms. Results: At 52, 78 and 104 weeks, 512, 483 and 464 eyes were included, respectively. For the training group, testing group and validation group, the respective correlation coefficients were 0.75, 0.77 and 0.70 (52 weeks); 0.79, 0.80 and 0.55 (78 weeks); and 0.83, 0.47 and 0.81 (104 weeks), while the mean standard errors of final visual acuity were 6.50, 6.11 and 6.40 (52 weeks); 5.91, 5.83 and 7.59; (78 weeks); and 5.39, 8.70 and 6.81 (104 weeks). Conclusions: Machine learning had good correlation coefficients for predicating prognosis with ranibizumab with just baseline characteristics. These models could be the useful clinical tools for prediction of success of the treatments.