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Background: In recent years, medical training has significantly increased the use of simulation for teaching and evaluation. The retraining of medical personnel in Italy is entrusted to the program of Continuous Education in Medicine, mainly based on theoretical training. The aim of this study is to assess whether the use of a new sensorized platform for the execution of the neonatal intubation procedure in simulation environment can complement theoretical retraining of experienced health professionals. Methods: Neonatal intubation tests were performed using a commercial manikin and a modified videolaryngoscope by the addition of force and position sensors, which provide the user with feedback when the threshold is exceeded. Two categories carried out the simulation tests: anesthesiologists and pediatricians. The categories were divided into three groups each, and various configurations were tested: the first group of both specialists carried out the tests without feedback (i.e. control groups, gr. A and A1), the second groups received sound and visual feedback from the instrument (gr. B and B1) and the third ones had also the support of a physician expert in the use of the instrument (gr. C and C1). The instrumentation used by pediatricians was provided in a playful form, including a game with increasing difficulty levels. Results: Both in the case with feedback only and in the case with humans support, anesthesiologists did not show a specific trend of improvement. Pediatricians, in comparison with anesthesiologists, showed a positive reaction to both the presence of feedback and that of experienced personnel. Comparing the performance of the two control groups, the two categories of experienced doctors perform similar forces. Pediatricians enjoyed the "Level Game", through which they were able to test and confront themselves, trying to improve their own performance. Conclusions: Our instrument is more effective when is playful and competitive, introducing something more than just a sound feedback, and allowing training by increasing levels. It is more effective if the users can adapt their own technique to the instrument by themselves, without any external help.

BackgroundSerious games, and especially digital game based learning (DGBL) methodologies, have the potential to strengthen classic learning methodology in all medical procedures characterized by a flowchart (e.g., neonatal resuscitation algorithm). However, few studies have compared short- and long-term knowledge retention in DGBL methodologies with a control group undergoing specialist training led by experienced operators. In particular, resident doctors' learning still has limited representation in simulation-based education literature.ObjectiveA serious computer game DIANA (DIgital Application in Newborn Assessment) was developed, according to newborn resuscitation algorithm, to train pediatric/neonatology residents in neonatal resuscitation algorithm knowledge and implementation (from procedure knowledge to ventilation/chest compressions rate). We analyzed user learning curves after each session and compared knowledge retention against a classic theoretical teaching session.MethodsPediatric/neonatology residents of the Azienda Ospedaliera Universitaria Pisana (AOUP) were invited to take part in the study and were split into a game group or a control group; both groups were homogeneous in terms of previous training and baseline scores. The control group attended a classic 80 min teaching session with a neonatal trainer, while game group participants played four 20 min sessions over four different days. Three written tests (pre/immediately post-training and at 28 days) were used to evaluate and compare the two groups' performances.ResultsForty-eight pediatric/neonatology residents participated in the study. While classic training by a neonatal trainer demonstrated an excellent effectiveness in short/long-term knowledge retention, DGBL methodology proved to be equivalent or better. Furthermore, after each game session, DGBL score improved for both procedure knowledge and ventilation/chest compressions rate.ConclusionsIn this study, DGBL was as effective as classic specialist training for neonatal resuscitation in terms of both algorithm memorization and knowledge retention. User appreciation for the methodology and ease of administration, including remotely, support the use of DGBL methodologies for pediatric/neonatology residents education.

Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery—69%, sub-intensive—82%, intensive—77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert’s annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment.