A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

Hydrogel Formulations Incorporating Drug Nanocrystals Enhance the Therapeutic Effect of Rebamipide in a Hamster Model for Oral Mucositis

A mouthwash formulation of rebamipide (REB) is commonly used to treat oral mucositis; however, this formulation does not provide sufficient treatment or prevention in cases of serious oral mucositis. To improve treatment, we attempted to design a hydrogel incorporating REB nanocrystals (R-NPs gel). The R-NPs gel was prepared by a bead mill method using carbopol hydrogel, methylcellulose and 2-hydroxypropyl-β-cyclodextrin, and another hydrogel incorporating REB microcrystals (R-MPs gel) was prepared following the same protocol but without the bead mill treatment. The REB particle size in the R-MPs gel was 0.15–25 μm, and while the REB particle size was 50–180 nm in the R-NPs gel. Next, we investigated the therapeutic effect of REB nanocrystals on oral mucositis using a hamster model. Almost all of the REB was released as drug nanocrystals from the R-NPs gel, and the REB content in the cheek pouch of hamsters treated with R-NPs gel was significantly higher than that of hamsters treated with R-MPs gel. Further, treatment with REB hydrogels enhanced the healing of oral wounds in the hamsters. REB accumulation in the cheek pouch of hamsters treated with the R-NPs gel was prevented by an inhibitor of clathrin-dependent endocytosis (CME) (40 μM dynasore). In conclusion, we designed an R-NPs gel and found that REB nanocrystals are taken up by tissues through CME, where they provide a persistent effect resulting in an enhancement of oral wound healing

Novel Sustained-Release Drug Delivery System for Dry Eye Therapy by Rebamipide Nanoparticles

The commercially available rebamipide ophthalmic suspension (CA-REB) was approved for clinical use in patients with dry eye; however, the residence time on the ocular surface for the traditional formulations is short, since the drug is removed from the ocular surface through the nasolacrimal duct. In this study, we designed a novel sustained-release drug delivery system (DDS) for dry eye therapy by rebamipide nanoparticles. The rebamipide solid nanoparticle-based ophthalmic formulation (REB-NPs) was prepared by a bead mill using additives (2-hydroxypropyl-β-cyclodextrin and methylcellulose) and a gel base (carbopol). The rebamipide particles formed are ellipsoid, with a particle size in the range of 40–200 nm. The rebamipide in the REB-NPs applied to eyelids was delivered into the lacrimal fluid through the meibomian glands, and sustained drug release was observed in comparison with CA-REB. Moreover, the REB-NPs increased the mucin levels in the lacrimal fluid and healed tear film breakup levels in an N-acetylcysteine-treated rabbit model. The information about this novel DDS route and creation of a nano-formulation can be used to design further studies aimed at therapy for dry eye

A comparative study of English and Japanese ChatGPT responses to anaesthesia-related medical questions

Background: The expansion of artificial intelligence (AI) within large language models (LLMs) has the potential to streamline healthcare delivery. Despite the increased use of LLMs, disparities in their performance particularly in different languages, remain underexplored. This study examines the quality of ChatGPT responses in English and Japanese, specifically to questions related to anaesthesiology. Methods: Anaesthesiologists proficient in both languages were recruited as experts in this study. Ten frequently asked questions in anaesthesia were selected and translated for evaluation. Three non-sequential responses from ChatGPT were assessed for content quality (accuracy, comprehensiveness, and safety) and communication quality (understanding, empathy/tone, and ethics) by expert evaluators. Results: Eight anaesthesiologists evaluated English and Japanese LLM responses. The overall quality for all questions combined was higher in English compared with Japanese responses. Content and communication quality were significantly higher in English compared with Japanese LLMs responses (both P<0.001) in all three responses. Comprehensiveness, safety, and understanding were higher scores in English LLM responses. In all three responses, more than half of the evaluators marked overall English responses as better than Japanese responses. Conclusions: English LLM responses to anaesthesia-related frequently asked questions were superior in quality to Japanese responses when assessed by bilingual anaesthesia experts in this report. This study highlights the potential for language-related disparities in healthcare information and the need to improve the quality of AI responses in underrepresented languages. Future studies are needed to explore these disparities in other commonly spoken languages and to compare the performance of different LLMs