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Branaghan, Russell J.; O’Brian, Joseph S.; Hildebrand, Emily A.; Foster, L. Bryant

doi:10.1007/978-3-030-64433-8_15

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…These studies fail to acknowledge that device noise is not the only common artifact present in experimental and clinical datasets. For example, the U.S. Food and Drug Administration estimates that as many as one in three device failures leading to adverse medical outcomes are not caused by an issue with the device itself 26 . Conversely, they were caused by improper use of a medical device (defined as “use-error”) 26 .…”

Section: Introductionmentioning

confidence: 99%

“…For example, the U.S. Food and Drug Administration estimates that as many as one in three device failures leading to adverse medical outcomes are not caused by an issue with the device itself. 26 Conversely, they were caused by improper use of a medical device (defined as “use-error”). 26 These errors are not necessarily caused by a lack of proper training.…”

Section: Introductionmentioning

confidence: 99%

“… 26 Conversely, they were caused by improper use of a medical device (defined as “use-error”). 26 These errors are not necessarily caused by a lack of proper training. For example, a 2003 study found that of 1000 hand surgeons—expert practitioners with years of specialized medical training—“20% of them admitted to having operated on the wrong site at least once in their career.” 26 Especially in a low-resource clinical setting, funding and resources to properly train clinicians on the proper use of every medical device they use may not be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

“… 26 These errors are not necessarily caused by a lack of proper training. For example, a 2003 study found that of 1000 hand surgeons—expert practitioners with years of specialized medical training—“20% of them admitted to having operated on the wrong site at least once in their career.” 26 Especially in a low-resource clinical setting, funding and resources to properly train clinicians on the proper use of every medical device they use may not be guaranteed. Beyond this, in both high and low-resource clinical settings, clinicians are often forced to make compromises regarding the use of various medications and medical devices.…”

Section: Introductionmentioning

confidence: 99%

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Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

Scarbrough,

Chen,

2024

J. Biomed. Opt.

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Significance: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, μ a and reduced scattering coefficient, μ 0 s . Aim: We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra.Approach: We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where μ a ¼ 0.44 to 2.45 cm −1 , and μ 0 s ¼ 6.53 to 9.58 cm −1 . We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model. Results:When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for μ a and 1.53% for μ 0 s , compared to the MCI's 50.9% for μ a and 24.6% for μ 0 s . Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for μ a and μ 0 s , respectively. The errors for MCI were about eight times higher. Conclusions:The WIR model presents reliable use-error-robust optical property predictions from DRS data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

Scarbrough,

Chen,

2024

J. Biomed. Opt.

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How to Design a Successful Training Application with Used Mobile Augmented Reality

Møsbæk,

Bjørner

2023

Lecture Notes in Computer Science

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An Augmented Reality Training Application for Service and Maintenance of a Medical Analyzer: A UX Approach to Usefulness and User Satisfaction

Mosbak

Bjørner

2022

2022 8th International Conference of the Immersive Learning Research Network (iLRN)

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This paper presents an experimental study on a mobile augmented reality application for immersive training of field engineers in service and maintenance of a medical analyzer (AQT90FLEX). Based on approaches from user experience design, we developed an AR training application with the aim of high level of relevance, ease of use, usefulness, user satisfaction, and learnability. Sixteen field engineers from the multinational company Radiometer participated in the study. The procedure was divided into three iterative stages: design, prototype, and evaluation. The methods consisted of questionnaires, interviews, and co-creation. The questionnaire was inspired by the technology acceptance model. The findings revealed that all the field engineers expressed positive feedback in terms of being able to see, train, and practice on the AQT90FLEX analyzer. Especially the usefulness and user satisfaction were positively evaluated. This study also shows the importance of using mixed methods, both qualitative and quantitative approaches, in order to develop an AR training application for field engineers across the world.

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Cited by 3 publications

References 23 publications

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra

How to Design a Successful Training Application with Used Mobile Augmented Reality

An Augmented Reality Training Application for Service and Maintenance of a Medical Analyzer: A UX Approach to Usefulness and User Satisfaction

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