03:45 PM Abstract No. 38 The development of a machine learning smart speaker application for device sizing in interventional radiology

Seals, Kevin; Al-Hakim, Ramsey; Mulligan, Patrick; Lehrman, Evan; Fidelman, Nicholas; Kolli, K. Pallav; Kohlbrenner, Ryan; Kohi, Maureen; Taylor, Amy

doi:10.1016/j.jvir.2018.12.077

Cited by 8 publications

(11 citation statements)

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“…A previous study suggested that the smart system using voice-controlled technology presents new opportunities for the care of diabetic patients having complications in their lower extremities [20]. Considering the aseptic conditions during an interventional radiology procedure, a machine learning smart speaker was developed to provide device information to the clinicians [11]. A more comprehensive study, using a smart speaker, was performed to predict cardiac arrests by using real-world 9-1-1 audio [13].…”

Section: Discussionmentioning

confidence: 99%

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Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study

Yoo

Oh²,

Kim

et al. 2020

PLoS ONE

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Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to review the patient's charts due to their aseptic hands. To improve the conditions in surgical time-outs, we introduce a deep learning-based smart speaker to confirm the surgical information prior to cataract surgeries. This pilot study utilized the publicly available audio vocabulary dataset and recorded audio data published by the authors. The audio clips of the target words, such as left, right, cataract, phacoemulsification, and intraocular lens, were selected to determine and confirm surgical information in the time-out speech. A deep convolutional neural network model was trained and implemented in the smart speaker that was developed using a mini development board and commercial speakerphone. To validate our model in the consecutive speeches during time-outs, we generated 200 time-out speeches for cataract surgeries by randomly selecting the surgical statuses of the surgical participants. After the training process, the deep learning model achieved an accuracy of 96.3% for the validation dataset of short-word audio clips. Our deep learning-based smart speaker achieved an accuracy of 93.5% for the 200 time-out speeches. The surgical and procedural accuracy was 100%. Additionally, on validating the deep learning model by using web-generated time-out speeches and video clips for general surgery, the model exhibited a robust and good performance. In this pilot study, the proposed deep learning-based smart speaker was able to successfully confirm the surgical information during the time-out speech. Future studies should focus on collecting real-world time-out data and automatically connecting the device to electronic health records. Adopting smart speaker-assisted time-out phases will improve the patients' safety during cataract surgeries, particularly in relation to wrong-site surgeries.

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Section: Discussionmentioning

confidence: 99%

“…Recently, a research group demonstrated the use of a smart speaker for interventional radiology procedures [11]. This device could capture a human voice and provide information about the intervention device sizing.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study

Yoo

Oh²,

Kim

et al. 2020

PLoS ONE

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“…In particular, the possibility of interrogating a previously instructed intelligent assistant to obtain suggestions on which device is most appropriate during a specific intervention procedure, before removing it from the sterile container, or what is the availability of the same in hospital stock or make a cost analysis with respect to another device, is even being studied by a group of researchers from the University of California. Its application could allow the operator to choose between two devices not only according to the appropriateness for the treatment but also in relation to the outcome data or patient-specific anatomy, optimizing results in terms of time and resource savings [ 55 ].…”

Section: Fields Of Applicationmentioning

confidence: 99%

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

et al. 2021

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Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process data, understand its meaning and provide the desired outcome, continuously redefining its logic. AI was mainly introduced via artificial neural networks, developed in the early 1950s, and with its evolution into "computational learning models." Machine Learning analyzes and extracts features in larger data after exposure to examples; Deep Learning uses neural networks in order to extract meaningful patterns from imaging data, even deciphering that which would otherwise be beyond human perception. Thus, AI has the potential to revolutionize the healthcare systems and clinical practice of doctors all over the world. This is especially true for radiologists, who are integral to diagnostic medicine, helping to customize treatments and triage resources with maximum effectiveness. Related in spirit to Artificial intelligence are Augmented Reality, mixed reality, or Virtual Reality, which are able to enhance accuracy of minimally invasive treatments in image guided therapies by Interventional Radiologists. The potential applications of AI in IR go beyond computer vision and diagnosis, to include screening and modeling of patient selection, predictive tools for treatment planning and navigation, and training tools. Although no new technology is widely embraced, AI may provide opportunities to enhance radiology service and improve patient care, if studied, validated, and applied appropriately.

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“…The strength of these technologies is their connection to the internet and ability to store data that can be accessed easily. Pilot studies have demonstrated the feasibility of such uses in various settings including providing device recommendations in the operation room [ 45 •], confirming surgical timeout data [ 46 •], and predicting cardiac arrests based on 911 audio conversations [ 47 •]. Hypothetical uses include confirming medication dosage and administration, facilitating communication between patients and hospital staff, and recording patient-reported outcomes.…”

Section: Current and Potential Uses Of Voice-based Technologies In Camentioning

confidence: 99%

Feasibility of Incorporating Voice Technology and Virtual Assistants in Cardiovascular Care and Clinical Trials

Gouda

Ganni

Chung

et al. 2021

Curr Cardiovasc Risk Rep

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Purpose of Review With the rising cost of cardiovascular clinical trials, there is interest in determining whether new technologies can increase cost effectiveness. This review focuses on current and potential uses of voice-based technologies, including virtual assistants, in cardiovascular clinical trials. Recent Findings Numerous potential uses for voice-based technologies have begun to emerge within cardiovascular medicine. Voice biomarkers, subtle changes in speech parameters, have emerged as a potential tool to diagnose and monitor many cardiovascular conditions, including heart failure, coronary artery disease, and pulmonary hypertension. With the increasing use of virtual assistants, numerous pilot studies have examined whether these devices can supplement initiatives to promote transitional care, physical activity, smoking cessation, and medication adherence with promising initial results. Additionally, these devices have demonstrated the ability to streamline data collection by administering questionnaires accurately and reliably. With the use of these technologies, there are several challenges that must be addressed before wider implementation including respecting patient privacy, maintaining regulatory standards, acceptance by patients and healthcare providers, determining the validity of voice-based biomarkers and endpoints, and increased accessibility. Summary Voice technology represents a novel and promising tool for cardiovascular clinical trials; however, research is still required to understand how it can be best harnessed.

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03:45 PM Abstract No. 38 The development of a machine learning smart speaker application for device sizing in interventional radiology

Cited by 8 publications

References 0 publications

Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study

Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice

Feasibility of Incorporating Voice Technology and Virtual Assistants in Cardiovascular Care and Clinical Trials

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