Convolution Neural Networks and Targeted Fluorescent Nanoparticles to Detect and ICDAS Score Caries.

Previous work has shown targeted fluorescent starch nanoparticles (TFSNs) can label the subsurface of carious lesions and assist dental professionals in the diagnostic process. In this study, we aimed to evaluate the potential of using artificial intelligence (AI) to detect and score carious lesions using ICDAS in combination with fluorescent imaging following application of TFSNs on teeth with a range of lesion severities, using ICDAS-labeled images as the reference standard. A total of 130 extracted human teeth with ICDAS scores from 0 to 6 were selected by a calibrated cariologist. Then, the same surface was imaged with a stereomicroscope under white light illumination, without visible fluorescence, and blue light illumination with an orange filter following application of the TFSNs. Both sets of images were labeled by another blinded ICDAS-calibrated cariologist to demarcate lesion position and severity. Convolutional neural networks, state-of-the-art models in imaging AI, were trained to determine the presence, location, ICDAS score (severity), and lesion surface porosity (as an indicator of activity) of carious lesions, and tested by 30 k-fold validation for white light, blue light, and the combined image sets. The best models showed high performance for the detection of carious lesions (sensitivity 80.26%, PPV 76.36%), potential for determining the severity via ICDAS scoring (accuracy 72%, SD 5.67%), and the detection of surface porosity as an indicator of the activity of the lesions (accuracy 90%, SD 7.00%). More broadly, the combination of targeted biopolymer nanoparticles with imaging AI is a promising combination of novel technologies that could be applied to many other applications.

Using natural language processing to compare task-specific verbal cues in coached versus noncoached cardiac arrest teams during simulated pediatrics resuscitation.

OBJECTIVES: Coaches improve cardiopulmonary (CPR) outcomes in real-world and simulated settings. To explore verbal feedback that targets CPR quality, we used natural language processing (NLP) methodologies on transcripts from a published pediatric randomized trial (coach vs. no coach in simulated CPR). Study objectives included determining any differences by trial arm in (1) overall communication and (2) metrics over minutes of CPR and (3) exploring overall frequencies and temporal patterns according to degrees of CPR excellence. METHODS: A human-generated transcription service produced 40 team transcripts. Automated text search with manual review assigned semantic category; word count; and presence of verbal cues for general CPR, compression depth or rate, or positive feedback to transcript utterances. Resulting cue counts per minute (CPM) were corresponded to CPR quality based on compression rate and depth per minute. CPMs were compared across trial arms and over the 18 min of CPR. Adaptation to excellence was analyzed across four patterns of CPR excellence determined by k-shape methods. RESULTS: Overall coached teams experienced more rate-directive, depth-directive, and positive verbal cues compared with noncoached teams. The frequency of coaches' depth cues changed over minutes of CPR, indicating adaptation. In coached teams, the number of depth-directive cues differed among the four patterns of CPR excellence. Noncoached teams experienced fewer utterances by type, with no adaptation over time or to CPR performance. CONCLUSION: NLP extracted verbal metrics and their patterns in resuscitation sessions provides insight into communication patterns and skills used by CPR coaches and other team members. This could help to further optimize CPR training, feedback, excellence, and outcomes.

Thoracic Trauma, Nonaortic Injuries.

Trauma is one of the leading causes of death worldwide. Approximately two-thirds of trauma patients have thoracic injuries. Nonvascular injury to the chest is most common; however, while vascular injuries to the chest make up a small minority of injuries in thoracic trauma, these injuries are most likely to require intervention by interventional radiology (IR). IR plays a vital role, with much to offer, in the evaluation and management of patients with both vascular and nonvascular thoracic trauma; in many cases, IR treatments obviate the need for these patients to go to the operating room. This article reviews the role of IR in the treatment of vascular an nonvascular traumatic thoracic injuries.

Machine learning to extract communication and history-taking skills in OSCE transcripts.

OBJECTIVES: Observed Structured Clinical Exams (OSCEs) allow assessment of, and provide feedback to, medical students. Clinical examiners and standardised patients (SP) typically complete itemised checklists and global scoring scales, which have known shortcomings. In this study, we applied machine learning (ML) to label some communication skills and interview content information in OSCE transcripts and to compare several ML methodologies by performance and transferability. METHODS: One-hundred and twenty-one transcripts of two OSCE scenarios were manually annotated per utterance across 19 communication skills and content areas. Utterances were converted to two types of numeric sentence vector representations and were paired with three types of ML algorithms. First, ML models (MLMs) were evaluated using a five K-fold cross-validation technique on all transcripts in one scenario to generate precision and recall, and their harmonic mean, F1 scores. Second, ML models were trained on all 101 transcripts from scenario 1 and tested for transferability on 20 scenario 2 transcripts. RESULTS: Performance testing in the K-fold cross-validation demonstrated relatively high mean F1 scores: median 0.87 and range 0.53-0.98 across all 19 labels. Transferability testing demonstrated success: F1 median 0.76 and range 0.46-0.97. The combination of a bi-directional long short-term memory neural network (biLSTM) algorithm with GenSen numeric sentence vector representations was associated with greater F1 scores across both performance and transferability (P < .005). CONCLUSIONS: We report the first application of ML in the context of student-SP OSCEs. We demonstrated that several MLMs automatically labelled OSCE transcripts for a range of interview content and some clinical communications skills. Some MLMs achieved greater performance and transferability. Optimised MLMs could provide automated and accurate assessment of OSCEs with potential to track student progress and identify areas for further practice.

Feasibility and Acceptability in a Community-Partnered Implementation of CenteringParenting for Group Well-Child Care.

BACKGROUND: In a community-academic partnership, we implemented a group-based model for well-child care (WCC) (CenteringParenting) and conducted a pilot test for feasibility and acceptability among families at a federally qualified health center (FQHC). METHODS: The FQHC implemented CenteringParenting for all WCC visits in the first year of life, starting at the 2-week visit. Over a 14-month time period, parents from each new CenteringParenting group were enrolled into the study. Baseline data were collected at enrollment (infant age < 31 days) and again at a 6-month follow-up survey. Main outcomes were feasibility and acceptability of CenteringParenting; we also collected exploratory measures (parent experiences of care, utilization, self-efficacy, and social support). RESULTS: Of the 40 parent-infant dyads enrolled in the pilot, 28 CenteringParenting participants completed the 6-month follow-up assessment. The majority of infants were Latino, black, or "other" race/ethnicity; over 90% were Medicaid insured. Of the 28 CenteringParenting participants who completed the 6-month follow-up, 25 completed all visits between ages 2 weeks and 6 months in the CenteringParenting group. Of the CenteringParenting participants, 97% to 100% reported having adequate time with their provider and sufficient patient education and having their needs met at visits; most reported feeling comfortable at the group visit, and all reported wanting to continue CenteringParenting for their WCC. CenteringParenting participants' mean scores on exploratory measures demonstrated positive experiences of care, overall satisfaction of care, confidence in parenting, and parental social support. CONCLUSIONS: A community-academic partnership implemented CenteringParenting; the intervention was acceptable and feasible for a minority, low-income population. We highlight key challenges of implementation.