The accuracy of an automatic free flap temperature monitor: a proof-of-concept study

Rendón-Medina, Marco Aurelio; Rendón-Pimentel, Marco Antonio; Palacios-Juárez, Julio

doi:10.1007/s00238-019-01583-1

Cited by 2 publications

(7 citation statements)

References 17 publications

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“…The oximeter and one thermometer is attached to the flap, while a second thermometer is attached to the adjacent skin to measure the differential temperature, as described in our previous work. 14 …”

Section: Methodsmentioning

confidence: 99%

“…In this model, we only measured the oximetry, and we previously performed an in vitro model to calculate the accuracy of the thermal sampling. 14 …”

Section: Methodsmentioning

confidence: 99%

“…This device provided a high specificity of 100% but, unfortunately, a low sensitivity of 7.99%. 14 In the future, we plan to combine the temperature and NIRS to create a multiparameter monitor. On this occasion, we worked with a NIRS sensor, GY-MAX30100 (Maxim Integrated, San Jose, Calif.), 15 to develop a device that monitors both temperature and oximetry.…”

Section: Introductionmentioning

confidence: 99%

“…This device provided a high specificity of 100% but, unfortunately, a low sensitivity of 7.99%. 14 In the future, we plan to combine the temperature and NIRS to create a multiparameter monitor. On this occasion, we worked Related Digital Media are available in the full-text version of the article on www.PRSGlobalOpen.com.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Predictive Values of an Automatic Device Measuring Oximetry in Free Flaps

Rendón-Medina

Galeana-Pavón

Vázquez-Medina

et al. 2021

Plastic and Reconstructive Surgery - Global Open

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Summary: Free-flap monitoring is challenging to perform in some centers. It requires the availability of trained health care personnel for 24 hours a day and seven days a week. Many methods had been proposed for flap monitoring, and none of them are superior to clinical evaluation. This study aimed to present a murine model to evaluate the accuracy (sensitivity, specificity, and the positive or negative predictive values) of a device. Wistar rats weighing 240–490 g were included for intervention and data collection. A murine model of left inferior epigastric vessel flaps was implemented. Intermittent pedicle clamping was performed to calculate the accuracy of the device that detects flow obstruction. The general variables studied were age, weight, and gender. The sensitivity, specificity, and negative or predictive values were calculated. The results showed a sensitivity of 97%, a specificity of 95% with a positive predictive value of 95%, and negative predictive value of 97%. The sensitivity and specificity showed excellent results within the range of clinical security. We require more data to analyze the multiparameter monitoring to see if it is feasible and cost-effective.

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Section: Methodsmentioning

confidence: 99%

“…In this model, we only measured the oximetry, and we previously performed an in vitro model to calculate the accuracy of the thermal sampling. 14 …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This device provided a high specificity of 100% but, unfortunately, a low sensitivity of 7.99%. 14 In the future, we plan to combine the temperature and NIRS to create a multiparameter monitor. On this occasion, we worked Related Digital Media are available in the full-text version of the article on www.PRSGlobalOpen.com.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Predictive Values of an Automatic Device Measuring Oximetry in Free Flaps

Rendón-Medina

Galeana-Pavón

Vázquez-Medina

et al. 2021

Plastic and Reconstructive Surgery - Global Open

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“…Some technologies have a long learning curve and require verification with other objective measures by experienced staff. 7–19 Direct clinical assessments remain the most convenient and widely accepted clinical practice. 20 Postoperative flap monitoring is labor-intensive and a continuous burden for staff.…”

mentioning

confidence: 99%

Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning

Huang

Tsai

Hsieh

et al. 2023

Plastic &Amp; Reconstructive Surgery

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Background: Postoperative free flap monitoring is a critical part of reconstructive microsurgery. Postoperative clinical assessments rely heavily on specialty-trained staff. Therefore, in regions with limited specialist availability, the feasibility of performing microsurgery is restricted. This study aimed to apply artificial intelligence in postoperative free flap monitoring and validate the ability of machine learning in predicting and differentiating types of postoperative free flap circulation. Methods: Postoperative data from 176 patients who received free flap surgery were prospectively collected, including free flap photographs and clinical evaluation measures. Flap circulation outcome variables included normal, arterial insufficiency, and venous insufficiency. The Synthetic Minority Oversampling Technique plus Tomek Links (SMOTE-Tomek) was applied for data balance. Data were divided into 80%:20% for model training and validation. Shapley Additive Explanations were used for prediction interpretations of the model. Results: Of 805 total included flaps, 555 (69%) were normal, 97 (12%) had arterial insufficiency, and 153 (19%) had venous insufficiency. The most effective prediction model was developed based on random forest, with an accuracy of 98.4%. Temperature and color differences between the flap and the surrounding skin were the most significant contributing factors to predict a vascular compromised flap. Conclusions: This study demonstrated the reliability of a machine-learning model in differentiating various types of postoperative flap circulation. This novel technique may reduce the burden of free flap monitoring and encourage the broader use of reconstructive microsurgery in regions with a limited number of staff specialists.

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The accuracy of an automatic free flap temperature monitor: a proof-of-concept study

Cited by 2 publications

References 17 publications

Evaluation of Predictive Values of an Automatic Device Measuring Oximetry in Free Flaps

Evaluation of Predictive Values of an Automatic Device Measuring Oximetry in Free Flaps

Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning

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