The Perioperative Human Digital Twin

Lonsdale, Hannah; Gray, Geoffrey M; Ahumada, Luis M.; Yates, Hannah M.; Varughese, Anna M.; Rehman, Mohamed A.

doi:10.1213/ane.0000000000005916

Cited by 27 publications

(24 citation statements)

References 62 publications

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“…Therefore there is a need to create expansive multivariate models that allow an algorithm’s prediction to incorporate patient risk profiles, with future studies developing a richer patient representation by incorporating features from EHRs and beyond. 56 In an early example of the potential of this technology, Isikli Esener et al 57 demonstrated that their novel proposed multistage classification scheme is more effective than the single-stage classification for computer-aided breast cancer diagnosis.…”

Section: Discussion: Challenges In Development and Adoption Of Machin...mentioning

confidence: 99%

“…The technical aspects of model-building and computing power will continue to foster new decision-support solutions. 56 The future education of clinicians must cover aspects of AI that will allow them to appraise and appropriately use these new tools. 60 This understanding will also aid acceptance into practice and accelerate widespread clinical use.…”

Section: Anesthesia and Analgesiamentioning

confidence: 99%

See 1 more Smart Citation

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Lonsdale,

Gray,

Ahumada

et al. 2023

Anesthesia &Amp; Analgesia

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Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision–based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

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Section: Discussion: Challenges In Development and Adoption Of Machin...mentioning

confidence: 99%

Section: Anesthesia and Analgesiamentioning

confidence: 99%

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Lonsdale,

Gray,

Ahumada

et al. 2023

Anesthesia &Amp; Analgesia

Self Cite

View full text Add to dashboard Cite

show abstract

“…In sum, the importance of building multi-modal network models has been discussed in the context of emerging field of network physiology (Bashan et al, 2012;Ivanov PCh, 2021) but also in the context of precision medicine and digital twins (Coorey et al, 2021;Björnsson et al, 2019;Lonsdale et al, 2022). Some promising and valuable computational models have been already developed in the field of human pain [see e.g., (Lang et al, 2021;Ionescu et al, 2019)] and a review of such models could help to understand, predict and analyze nociceptive system function and pain perception.…”

Section: Discussionmentioning

confidence: 99%

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

Kutafina

Becker²,

Namer³

2023

Front. Netw. Physiol.

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In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

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“…Machine learning is used increasingly for predicting outcomes in medicine 13 because it can compute complex nonlinear relationships across multiple variables and generate tailored recommendations for individual patients on a precision medicine basis. 14 We leveraged the APRICOT dataset with machine learning techniques to develop models that can determine which pediatric patients in ASA-PS class I-III are at low risk for severe perioperative critical events. We identified two time points in the clinical pathway at which individualized risk stratification would have utility.…”

Section: The Apricot (Anaesthesia Practice In Children Observationalmentioning

confidence: 99%

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

Gray

Ahumada

Rehman

et al. 2023

Pediatric Anesthesia

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Background Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at‐risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA‐PS) score, despite reported inconsistencies with this method. Aims The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. Methods Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA‐PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA‐PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. Results Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6–0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day‐of‐surgery phase. Conclusions This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population‐based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

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The Perioperative Human Digital Twin

Cited by 27 publications

References 62 publications

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

A machine‐learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

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