Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Assessment of the Clinical Performance of Platelet Concentrates Treated by Pathogen Reduction Technology in Santiago de Compostela.

INTRODUCTION: This study assessed the feasibility, performance, and safety of Mirasol®-treated platelet concentrates (M-PC) stored for up to 7 days. METHODS: This prospective observational study was approved by the ethical committee of the University Clinic of Santiago de Compostela. Informed consent was asked from patients receiving M-PC. M-PCs were treated with the Mirasol system according to the manufacturer's instructions. Thrombocytopenic patients were transfused according to the Spanish transfusion guidelines. Post-transfusion platelet counts were measured at 1 h and/or 24 h after transfusion. Post-transfusion surveillance of patients was maintained during the study. RESULTS: Data from 54 evaluable patients and 135 transfusions were analyzed. The mean age of patients was 58 years. The mean age of M-PC at transfusion was 3.6 days. The mean platelet dose was 3.7 × 1011. The transfusion responses measured as mean corrected count increment 1 h after transfusion (CCI1h) and CCI24h were 9,659 and 4,751, respectively. 65% of transfusions resulted in CCI1h values ≥ 7,500. 51% of transfusions resulted in CCI24h values ≥ 4,500. CONCLUSION: The use of M-PC in the supportive treatment proved to be safe and effective for this cohort of thrombocytopenic patients.

A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology.

Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.

Study protocol: A comparison of mobile and clinic-based spirometry for capturing the treatment effect in moderate asthma.

Several inefficiencies in drug development trial implementation may be improved by moving data collection from the clinic to mobile, allowing for more frequent measurements and therefore increased statistical power while aligning to a patient-centric approach to trial design. Sensor-based digital health technologies such as mobile spirometry (mSpirometry) are comparable to clinic spirometry for capturing outcomes, such as forced expiratory volume in 1 s (FEV1); however, the impact of remote spirometry measurements on the detection of treatment effect has not been investigated. A protocol for a multicenter, single-arm, open-label interventional trial of long-acting beta agonist (LABA) therapy among 60 participants with uncontrolled moderate asthma is described. Participants will complete twice-daily mSpirometry at home and clinic spirometry during weekly visits, alongside continuous use of a wrist-worn wearable and regular completion of several diaries capturing asthma symptoms as well as participant- and site-reported satisfaction and ease of use of mSpirometry. The co-primary objectives of this study are (A) to quantify the treatment effect of LABA therapy among participants with moderate asthma, using both clinical spirometry (FEV1c ) and mSpirometry (FEV1m ); and (B) to investigate whether FEV1m is as accurate as FEV1c in detecting the treatment effect using a mixed-effect model for repeated measures. Study results will help inform whether the deployment of mSpirometry and a wrist-worn wearable for remote data collection are feasible in a multicenter setting among participants with moderate asthma, which may then be generalizable to other populations with respiratory disease.

Feasibility and Impact of Integrating an Artificial Intelligence-Based Diagnosis Aid for Autism Into the Extension for Community Health Outcomes Autism Primary Care Model: Protocol for a Prospective Observational Study.

BACKGROUND: The Extension for Community Health Outcomes (ECHO) Autism Program trains clinicians to screen, diagnose, and care for children with autism spectrum disorder (ASD) in primary care settings. This study will assess the feasibility and impact of integrating an artificial intelligence (AI)-based ASD diagnosis aid (the device) into the existing ECHO Autism Screening Tool for Autism in Toddlers and Young Children (STAT) diagnosis model. The prescription-only Software as a Medical Device, designed for use in children aged 18 to 72 months at risk for developmental delay, produces ASD diagnostic recommendations after analyzing behavioral features from 3 distinct inputs: a caregiver questionnaire, 2 short home videos analyzed by trained video analysts, and a health care provider questionnaire. The device is not a stand-alone diagnostic and should be used in conjunction with clinical judgment. OBJECTIVE: This study aims to assess the feasibility and impact of integrating an AI-based ASD diagnosis aid into the ECHO Autism STAT diagnosis model. The time from initial ECHO Autism clinician concern to ASD diagnosis is the primary end point. Secondary end points include the time from initial caregiver concern to ASD diagnosis, time from diagnosis to treatment initiation, and clinician and caregiver experience of device use as part of the ASD diagnostic journey. METHODS: Research participants for this prospective observational study will be patients suspected of having ASD (aged 18-72 months) and their caregivers and up to 15 trained ECHO Autism clinicians recruited by the ECHO Autism Communities research team from across rural and suburban areas of the United States. Clinicians will provide routine clinical care and conduct best practice ECHO Autism diagnostic evaluations in addition to prescribing the device. Outcome data will be collected via a combination of electronic questionnaires, reviews of standard clinical care records, and analysis of device outputs. The expected study duration is no more than 12 months. The study was approved by the institutional review board of the University of Missouri-Columbia (institutional review board-assigned project number 2075722). RESULTS: Participant recruitment began in April 2022. As of June 2022, a total of 41 participants have been enrolled. CONCLUSIONS: This prospective observational study will be the first to evaluate the use of a novel AI-based ASD diagnosis aid as part of a real-world primary care diagnostic pathway. If device integration into primary care proves feasible and efficacious, prolonged delays between the first ASD concern and eventual diagnosis may be reduced. Streamlining primary care ASD diagnosis could potentially reduce the strain on specialty services and allow a greater proportion of children to commence early intervention during a critical neurodevelopmental window. TRIAL REGISTRATION: ClinicalTrials.gov NCT05223374; https://clinicaltrials.gov/ct2/show/NCT05223374. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37576.