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Background: Pneumonia is a leading cause of mortality in children aged <5 years. While machine learning (ML) has been applied to pneumonia diagnostics, few studies have focused on predicting the need for escalation of care in pediatric cases. This study aims to develop an ML-based clinical decision support tool for predicting the need for escalation of care in community-acquired pneumonia cases.

Objective: The primary objective was to develop a robust predictive tool to help primary care physicians determine where and how a case should be managed.

Methods: Data from 437 children with community-acquired pneumonia, collected before the COVID-19 pandemic, were retrospectively analyzed. Pediatricians encoded key clinical features from unstructured medical records based on Integrated Management of Childhood Illness guidelines. After preprocessing with Synthetic Minority Oversampling Technique-Tomek to handle imbalanced data, feature selection was performed using Shapley additive explanations values. The model was optimized through hyperparameter tuning and ensembling. The primary outcome was the level of care severity, defined as the need for referral to a tertiary care unit for intensive care or respiratory support.

Results: A total of 437 cases were analyzed, and the optimized models predicted the need for transfer to a higher level of care with an accuracy of 77% to 88%, achieving an area under the receiver operator characteristic curve of 0.88 and an area under the precision-recall curve of 0.96. Shapley additive explanations value analysis identified hypoxia, respiratory distress, age, weight-for-age z score, and complaint duration as the most important clinical predictors independent of laboratory diagnostics.

Conclusions: This study demonstrates the feasibility of applying ML techniques to create a prognostic care decision tool for childhood pneumonia. It provides early identification of cases requiring escalation of care by combining foundational clinical skills with data science methods.

Background: The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors.

Objective: This study aimed to develop a checklist that addresses the sociotechnical aspects of AI deployment in health care and provides a structured, holistic guide for teams involved in the life cycle of AI systems.

Methods: A literature synthesis identified 20 relevant studies, forming the foundation for the Clinical AI Sociotechnical Framework checklist. A modified Delphi study was then conducted with 35 global health care professionals. Participants assessed the checklist's relevance across 4 stages: "Planning," "Design," "Development," and "Proposed Implementation." A consensus threshold of 80% was established for each item. IQRs and Cronbach α were calculated to assess agreement and reliability.

Results: The initial checklist had 45 questions. Following participant feedback, the checklist was refined to 34 items, and a final round saw 100% consensus on all items (mean score >0.8, IQR 0). Based on the outcome of the Delphi study, a final checklist was outlined, with 1 more question added to make 35 questions in total.

Conclusions: The Clinical AI Sociotechnical Framework checklist provides a comprehensive, structured approach to developing and implementing AI in clinical settings, addressing technical and social factors critical for adoption and success. This checklist is a practical tool that aligns AI development with real-world clinical needs, aiming to enhance patient outcomes and integrate smoothly into health care workflows.

Intro: Breast cancer is the leading cause of morbidity and mortality worldwide. Accurate sentinel lymph node (SLN) mapping is crucial for staging and treatment planning in early-stage breast cancer. Indocyanine green (ICG) has emerged as a promising agent for fluorescence imaging in SLN mapping. However, comprehensive assessment of its clinical utility, including accuracy and adverse effects, remains limited. This scoping review aims to consolidate evidence on the use of ICG in breast cancer SLN mapping.

Objective: The objective of this scoping review is to evaluate the current literature on the use of ICG in SLN mapping for patients with breast cancer. This review aims to assess the accuracy, efficacy, and safety of ICG in this context and to identify gaps in the existing research. The outcomes will contribute to the development of further research as part of a PhD project.

Methods: Five electronic databases will be searched (PubMed, Embase, MEDLINE, Web of Science, and Scopus) using search strategies developed in consultation with an academic supervisor. The search strategy is set to human studies published in English within the last 11 years. All retrieved citations will be imported to Zotero and then uploaded to Covidence for the screening of titles, abstracts, and full text according to prespecified inclusion criteria. Patients with early-stage breast cancer (T1 and T2), selected T3 cases where the SLN biopsy is accurate, and those with clinically node-negative breast cancer will be included. The intervention criterion includes studies using ICG for SLN mapping and studies on the assessment of fluorescence imaging cameras. Citations meeting the inclusion criteria for full-text review will have their data extracted by 2 independent reviewers, with disagreements resolved by discussion. A data extraction tool will be developed to capture full details about the participants, concept, and context, and findings relevant to the scoping review will be summarized.

Results: The preliminary search began in December 2023. As of September 2024, papers have been screened and data are currently being extracted. Out of the 2130 references initially imported, 126 studies met the inclusion criteria after screening. The scoping review is expected to be published in January 2025.

Conclusions: Although ICG technology has been used for SLN mapping in patients with breast cancer, initial searches in 2022 revealed limited data on this technique's feasibility, safety, and effectiveness. At that time, preliminary search of Scopus, MEDLINE, Embase, and PubMed identified no current or forthcoming systematic reviews or scoping reviews on the topic. However, recent searches indicate a substantial increase in research and reviews, reflecting a growing interest and evidence in this area.

Background: Dance has emerged as a complementary treatment that may promote adaptive neural plasticity while improving symptoms of Parkinson disease (PD), such as balance, gait, posture, and walking. Understanding brain changes that arise from participation in dance interventions is important as these neural plastic changes play an important role in protecting and healing the brain. Although dance has been shown to improve PD motor and nonmotor symptoms, the neural mechanisms underlying these changes, specifically depression and mood, remain elusive. Further, many side effects of PD drug treatments can be exacerbated or even induced by dopaminergic drugs, particularly depression and anxiety, making these nonmotor symptoms more noticeable throughout the progression of the disease.

Objective: In this study, we focused on the impact of dance interventions on PD nonmotor symptoms by conducting an 8-month observational study, tracking the relationship between depression scores and functional neuroimaging measures for people with PD.

Methods: A total of 34 dancers-23 (68%) people with PD and 11 (32%) healthy controls-completed the Geriatric Depression Scale (GDS) before and after attending weekly community-based dance classes, referred to as Dance for PD classes. Specifically, we examined changes within the functional magnetic resonance imaging signal from the subcallosal cingulate gyrus (SCG), an important node within the depression network and a controversial target for deep brain stimulation in the treatment of major depressive disorder.

Results: Depression scores on the GDS decreased in each preintervention to postintervention comparison (all P<.025). In addition, GDS scores also improved over the 8-month dance period (all P<.01). Blood oxygen level-dependent signals from frontal cortex brain region implicated for emotional regulation within the SCG decreased at each testing time point (all P<.05). Also, a significant decrease in depression scores (GDS) was correlated with reduced blood oxygen level-dependent signals from the SCG (P=.02).

Conclusions: This study contributes to an improved understanding of the neural mechanisms that are involved in depression, as well as the beneficial contribution that longitudinal dance interventions have in reducing nonmotor symptoms associated with PD, particularly in depression symptoms.