Mobile Smartphone-Based Digital Pupillometry Curves in the Diagnosis of Traumatic Brain Injury

McGrath, Lynn B.; Eaton, Jessica; Abecassis, Isaac J.; Maxin, Anthony; Kelly, Cory M.; Chesnut, Randall M.; Levitt, Michael R.

doi:10.3389/fnins.2022.893711

Cited by 15 publications

(2 citation statements)

References 34 publications

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“…Furthermore, there is a need to develop technological approaches to improve the role of telemedicine platforms to act as a diagnostic platform as well as a medium for consultations when delivering TMOUD. Proposals for this include adapting smartphone-based pupillometry techniques to assess the degree of withdrawal from opioids [ 105 ] and a process for web-based point-of-care drug testing [ 106 ]. Although it is recognized that frontline practitioners and people who use drugs should be partners in the design, implementation, and evaluation of TMOUD [ 48 ], there is little evidence on how to engage these groups in a meaningful and impactful way.…”

Section: Discussionmentioning

confidence: 99%

Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants

Tay Wee Teck,

Gittins,

Zlatkute

et al. 2023

JMIR Ment Health

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Background Telemedicine-delivered medication for opioid use disorder (TMOUD) has become more prevalent during the COVID-19 pandemic, particularly in North America. This is considered a positive development as TMOUD has the potential to increase access to evidence-based treatment for a population heavily affected by the opioid crisis and consequent rising mortality and morbidity rates in relation to opioid use disorder. Despite the increase in the use of TMOUD, there are no established service- and process-focused models to guide the implementation of this intervention. Objective This study aims to develop a process- and service-focused implementation model in collaboration with key stakeholders and bring together peer-reviewed literature, practice-based knowledge, and expert opinions. Methods The simple rules for evidence translation in complex systems framework was applied to guide the development of a 6-step qualitative study. The steps were definition of the scope and objectives of the model, identification of evidence, stakeholder engagement, draft model development, key informant consultation, and final model specification. Results The final specification for the TMOUD implementation model incorporated key strategic priorities, service delivery prerequisites, service design elements, stakeholder identification and engagement, key process domains, and iterative cycles of evaluation and improvement. Conclusions Through stakeholder engagement and key informant consultation, we produced a process- and service-focused TMOUD implementation model. The model is modifiable to different contexts and settings while also in keeping with the current evidence base and national and international standards of high-quality opioid use disorder care.

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Section: Discussionmentioning

confidence: 99%

Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants

Tay Wee Teck,

Gittins,

Zlatkute

et al. 2023

JMIR Ment Health

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“…With the nearly universal availability of smartphones, they have found innovative uses in various clinical scenarios. [ 18 19 20 21 ] Recently, several smartphone-based pupillometer applications have been introduced to assess the absolute pupil measurements as well as the relative change in pupil size using the device’s built-in flash. Smartphone pupillometers have the advantage of being more affordable, portable, and accessible.…”

Section: Introductionmentioning

confidence: 99%

Interdevice Agreement between a Smartphone and a Commercial Pupillometer

Rattan,

Girgla,

Mahajan

et al. 2024

International Journal of Applied &Amp; Basic Medical Research

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Background: The reliability of dynamic pupillometry parameters varies from one pupillometer to another, making it difficult to standardize the values for any particular device. Hence, further studies are required to evaluate the agreement of various pupillometer devices and explore their utility in routine clinical settings. Aim: This study sought to evaluate the agreement between smartphone and commercial pupillometer measurements in routine clinical settings. Methods: The study included pupillary measurements obtained by a single investigator from 100 healthy participants (200 eyes) with each pupillometer. Pupillary measurements taken by a smartphone pupillometry application (reflex pupillary light reflex analyzer by Brightlamp [Indianapolis, IN, USA]) were compared with a commercial pupillometer (neurological pupil index-200, NeurOptics Inc., Irvine, USA). Results: The comparison of descriptive statistics revealed a statistically significant difference between the smartphone and commercial pupillometers for various parameters, including maximum diameter, minimum diameter, constriction velocity (CV), maximum CV, and dilatation velocity (P < 0.05), except for latency (P = 0.36). The intraclass correlation coefficient revealed poor agreement between the two devices (<0.50). Conclusion: The measurements by smartphone pupillometry application were found to be unreliable, indicating that they may not be an ideal substitute for commercial pupillometers in their present form in the Indian population. Further studies with larger sample size as well as improvements in the processing and interpretation of the measurements by the software, are needed to determine its utility in routine clinical settings.

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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review

Toy,

Warren,

Wilhelm

et al. 2024

JACEP Open

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BackgroundArtificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care.MethodsWe conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full‐text analysis. We included original research and conference presentations evaluating AI models—machine learning (ML), deep learning (DL), and natural language processing (NLP)—that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics.ResultsWe identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full‐text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life‐saving interventions (29%), assist in triage (22%), and predict survival (20%).ConclusionsA small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.

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Mobile Smartphone-Based Digital Pupillometry Curves in the Diagnosis of Traumatic Brain Injury

Cited by 15 publications

References 34 publications

Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants

Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants

Interdevice Agreement between a Smartphone and a Commercial Pupillometer

Use of artificial intelligence to support prehospital traumatic injury care: A scoping review

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