Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

The utility of wearable devices in the perioperative period

PURPOSE OF REVIEW

Improved perioperative patient monitoring is a crucial step toward better predicting postoperative outcomes. Wearable devices capable of measuring various health-related metrics represent a novel tool that can assist healthcare providers. However, the literature surrounding wearables is wide-ranging, preventing clinicians from drawing definitive conclusions regarding their utility. This review intends to consolidate the recent literature on perioperative wearables and summarize the most salient information.

RECENT FINDINGS

Wearable devices measuring cardiac output and colonic motility have recently been piloted with mixed results. Novel measurement techniques for established metrics have also been studied, including photoplethysmography devices for heart rate and blood pressure along with resistance thermometers for temperature. Nuanced methods of synthesizing data have been piloted, including machine-learning algorithms for predicting adverse events and trajectory curves for step count progression. Wearable devices are generally well accepted, although adjuvant support systems have improved patient satisfaction.

SUMMARY

Perioperative wearables are valuable tools for tracking postoperative health metrics, predicting adverse events, and improving patient satisfaction. Future research on removing barriers such as technological illiteracy, artifact generation, and false-positive alarms would enable better integration of wearables into the hospital setting.

Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions

Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, developmental variability, and ethical considerations. This narrative review explores the current trends, applications, challenges, and future directions of ML in pediatric healthcare. Methods: A systematic search of the PubMed database was conducted using the query: ("artificial intelligence" OR "machine learning") AND ("pediatric" OR "paediatric"). Studies were reviewed to identify key themes, methodologies, applications, and challenges. Gaps in the research and ethical considerations were also analyzed to propose future research directions. Results: ML has demonstrated promise in diagnostic support, prognostic modeling, and therapeutic planning for pediatric patients. Applications include the early detection of conditions like sepsis, improved diagnostic imaging, and personalized treatment strategies for chronic conditions such as epilepsy and Crohn's disease. However, challenges such as data limitations, ethical concerns, and lack of model generalizability remain significant barriers. Emerging techniques, including federated learning and explainable AI (XAI), offer potential solutions. Despite these advancements, research gaps persist in data diversity, model interpretability, and ethical frameworks. Conclusions: ML offers transformative potential in pediatric healthcare by addressing diagnostic, prognostic, and therapeutic challenges. While advancements highlight its promise, overcoming barriers such as data limitations, ethical concerns, and model trustworthiness is essential for its broader adoption. Future efforts should focus on enhancing data diversity, developing standardized ethical guidelines, and improving model transparency to ensure equitable and effective implementation in pediatric care.

Overcoming biases of individual level shopping history data in health research

Novel sources of population data, especially administrative and medical records, as well as the digital footprints generated through interactions with online services, present a considerable opportunity for advancing health research and policymaking. An illustrative example is shopping history records that can illuminate aspects of population health by scrutinizing extensive sets of everyday choices made in the real world. However, like any dataset, these sources possess specific limitations, including sampling biases, validity issues, and measurement errors. To enhance the applicability and potential of shopping data in health research, we advocate for the integration of individual-level shopping data with external datasets containing rich repositories of longitudinal population cohort studies. This strategic approach holds the promise of devising innovative methodologies to address inherent data limitations and biases. By meticulously documenting biases, establishing validated associations, and discerning patterns within these amalgamated records, researchers can extrapolate their findings to encompass population-wide datasets derived from national supermarket chain. The validation and linkage of population health data with real-world choices pertaining to food, beverages, and over-the-counter medications, such as pain relief, present a significant opportunity to comprehend the impact of these choices and behavioural patterns associated with them on public health.

Machine Learning Models and Applications for Early Detection

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

Improving Early Prediction of Abnormal Recovery after Appendectomy in Children using Real-world Data from Wearables

Postoperative complications are primary concerns during early recovery from pediatric appendectomy. Identifying complications or symptoms of abnormal recovery typically relies on intermittent and subjective assessments from children and their caregivers, which may result in delayed diagnosis. Wearable devices can capture continuous and objective health measurements, which can be mined for early biomarkers of complications or symptoms using machine learning models. However, real-world datasets from wearables often have missing and imbalanced data, which can affect model performance and utility. We have recorded real-world Fitbit data from 93 children during the first 21 days following appendectomy for complicated appendicitis. This dataset included missing data (37.0% across all participants) and imbalanced data (2.7% of total days recorded from children exhibiting abnormal recovery). Aiming to improve early prediction of abnormal recovery, we extracted 143 daily features from the data, including Fitbit metrics of activity, heart rate, and sleep, as well as metrics derived from clinical knowledge. We trained a Balanced Random Forest classifier and tested different early prediction strategies for identifying abnormal recovery (complications or abnormal symptoms) 1-3 days before they were clinically diagnosed. The best-performing model predicted abnormal recovery three days before diagnosis at an accuracy of 87.5%, two days before at 76.4%, one day before at 85.7%, and on the day of diagnosis at 78.8%. The overall prediction accuracy was improved 10.1% compared to a previous study. With further development, this approach could be used to generate near real-time alerts of abnormal postoperative recovery to enhance pediatric care and clinical decision making.

Current use of artificial intelligence in the diagnosis and management of acute appendicitis

INTRODUCTION

Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives.

EVIDENCE ACQUISITION

We performed a literature search on articles published from 2018 to September 2023. We included only original articles.

EVIDENCE SYNTHESIS

Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis.

CONCLUSIONS

AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

Step cadence as a novel objective postoperative recovery metric in children who undergo laparoscopic appendectomy

BACKGROUND

Daily step counts from consumer wearable devices have been used to objectively assess postsurgical recovery in children. However, step cadence, defined as steps taken per minute, may be a more specific measure of physiologic status. The purpose of this study is to define objective normative physical activity recovery trajectories after laparoscopic appendectomy using this novel metric. We hypothesized that patients would have a progressive increase in peak cadence until reaching a plateau representing baseline status, and this would occur earlier for simple compared with complicated appendicitis.

METHODS

Children aged 3 to 18 years were enrolled after laparoscopic appendectomy for simple or complicated appendicitis between March 2019 and December 2022 at a tertiary children's hospital. Participants wore a Fitbit for 21 postoperative days. The peak 1-minute cadence and peak 30-minute cadence were determined each postoperative day. Piecewise linear regression was conducted to generate normative peak step cadence recovery trajectories for simple and complicated appendicitis.

RESULTS

A total of 147 children met criteria (53.7% complicated appendicitis). Patients with simple appendicitis reached plateau postoperative day 10 at a mean peak 1-minute cadence of 111 steps/minute and a mean peak 30-minute cadence of 77 steps/minute. The complicated appendicitis recovery trajectory reached a plateau postoperative day 13 at a mean peak 1-minute cadence of 106 steps/minute and postoperative day 15 at a mean peak 30-minute cadence of 75 steps/minute.

CONCLUSION

Using step cadence, we defined procedure-specific normative peak cadence recovery trajectories after laparoscopic appendectomy. This can empower clinicians to set data-driven expectations for recovery after surgery and establish the groundwork for consumer wearable devices as a post-discharge remote monitoring tool.

Feasibility of Leveraging Consumer Wearable Devices with Data Platform Integration for Patient Vital Monitoring in Low-Resource Settings

Manual monitoring of vital signs, which often fails to capture the onset of deterioration, is the main monitoring modality in most Ghanaian hospitals due to the high cost and inadequate supply of patient bedside monitors. Consumer wearable devices (CWDs) are emerging, relatively low-cost technologies for continuous monitoring of physiological status; however, their validity has not been established in low-resource clinical settings. We aimed to (1) investigate the validity of the heart rate (HR) and oxygen saturation (SpO2) data from two widely used CWDs, the Fitbit Versa 2 and Xiaomi Mi Smart Band 6, against gold standard bedside monitors in one Ghanaian hospital and (2) develop a web application to capture and display CWD data in a clinician-friendly way. A healthy volunteer simultaneously wore both CWDs and blood pressure cuffs to measure HR and SpO2. To test for concordance, we conducted the Bland-Altman and mean absolute percentage error analyses. We also developed a web application that retrieves and displays CWD data in near real time as text and graphical trends. Compared to gold standards (patient monitor and manual), the Fitbit Versa 2 had 96.87% and 96.67% measurement accuracies for HR, and the Xiaomi Mi Smart Band 6 had 94.24% and 93.21% measurement accuracies for HR. The Xiaomi Mi Smart Band 6 had 98.79% measurement accuracy for SpO2. The strong concordance between CWD and gold standards supports the potential implementation of these devices as a novel method of vital sign monitoring to replace manual monitoring, thus saving costs and improving patient outcomes. Further studies are needed for confirmation.

AI in Rehabilitation Medicine: Opportunities and Challenges

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.