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The use of patient-specific organ models in three-dimensional printing systems could be helpful for the education of patients and medical students. The aim of this study was to clarify whether the use of patient-specific stoma models is helpful for patient education. From January 2014 to September 2014, 5 patients who underwent colorectal surgery and for whom a temporary or permanent stoma had been created were involved in this study. Three-dimensional stoma models and three-dimensional face plates were created. The patients’ ages ranged from 59 to 81 years. Four patients underwent stoma construction because of rectal cancer, and 1 underwent stoma construction because of colon stenosis secondary to recurrent cancer. All patients were educated about their stoma and potential stoma-associated problems using three-dimensional stoma models, and all practiced cutting face plates using three-dimensional face plates. The models were also used during medical staff conferences to discuss current issues. All patients understood their problems and finally became self-reliant. The recent availability of three-dimensional printers has enabled the creation of many organ models, and full-scale stoma and face plate models are now available for patient education on cutting an appropriately individualized face plate. Thus, three-dimensional printers could enable fewer skin problems than are currently associated with daily stomal care.

The study on K-12 artificial intelligence (AI) education poses three challenges: ''(i) What topics in the machine learning (ML) process should be hidden from K-12 students?” '' (ii) How do you teach K-12 students the mathematical model behind the ML process?” and '' (iii) How does AI education influence the acceptance of AI technology?”. This paper addresses challenges (i) to (iii). We developed a machine learning role-playing game (ML-RPG) and had 166 participants from lower grades of elementary school to the elderly use it. Participants used ML-RPG to role-play the ML process of acquiring data, representing data with graphs, inferencing based on if-then rules, and optimizing parameters based on loss functions in the ML model. Challenge (i) was assessed using an achievement test. The results showed that the ML process of perception at K1-3, data representation and reasoning based on if-then rules at K4-6, and simple ML model learning and evaluation at K7+ can be unveiled. Challenge (ii), K7+ were able to understand the optimization process of the ML model by role-playing to minimize the loss function shown on the graph. Challenge (iii), it was shown that most people who feel vague anxiety and fear about AI change to a positive impression of AI due to the following three factors: (a) participants understanding the decision criteria of AI, (b) increased self-efficacy among participants, and (c) participants realizing that AI can be applied to familiar things.