Influence of Artificial Intelligence on the Adenoma Detection Rate throughout the Day.

BACKGROUND: Artificial intelligence systems recently demonstrated an increase in polyp and adenoma detection rate. Over the daytime, the adenoma detection rate decreases as tiredness leads to a lack of attention. It is not clear if a polyp detection system with artificial intelligence leads to constant adenoma detection over the day. METHODS: We performed a database analysis of screening and surveillance colonoscopies with and without the use of AI. In both groups, patients were investigated with the same endoscopy equipment and by the same endoscopists. Only patients with good bowel preparation (BBPS >6) were included. We correlated the daytime, the investigational time, day of the week, and the adenoma and polyp detection. RESULTS: A total of 303 colonoscopies were analyzed. 163 endoscopies in the AI+ group and 140 procedures in the AI- group were included. In both groups, the total adenoma detection rate was equal (AI+ 0.39 vs. AI- 0.43). The adenoma detection rate throughout the day had a significant decreasing trend in the group without the use of AI (p = 0.015), whereas this trend was not present in the investigations that have been performed with AI (p = 0.65). The duration of investigation did not show a significant difference between the groups (8.9 min in both groups). No relevant effect was noticed in adenoma detection between single days of the working week with or without the use of AI. CONCLUSION: AI helps overcome the decay in adenoma detection over the daytime. This may be attributed to a constant awareness caused by the use of the AI system.

Human-machine-human interaction in motor control and rehabilitation: a review.

BACKGROUND: Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad's ability to accomplish a joint motor task (task performance) beyond either partner's ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area. METHODS: A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis. RESULTS: Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning. CONCLUSIONS: Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.