Texture and color enhancement imaging (TXI) has been developed as an image-enhanced endoscopy technology. TXI mode2 enhances texture and brightness, and TXI mode1 also enhances color. This study aims to assess the color differences in squamous cell carcinoma (SCC) suspicious lesions in the pharynx and esophagus using white light imaging (WLI), TXI mode1, TXI mode2, and narrow-band imaging (NBI). A total of 59 SCC suspicious lesions from 30 patients were analyzed. The color differences (ΔE) between the lesion and the surrounding mucosa were calculated for each modality. The color value was assessed using the Commission Internationale d’Eclairage L*a*b* color space. The visibility of the lesion in each modality was evaluated and compared to that in the WLI by six endoscopists. The mean ΔE values in the WLI, TXI mode1, TXI mode2, and NBI were 11.6; 18.6; 14.3; and 17.2, respectively, and the ΔE values of TXI mode1, TXI mode2, and NBI were significantly higher than those of the WLI (p < 0.001). No lesions had worse visibility, and 62.5% (37/59) had improved visibility, as assessed by more than half of the endoscopists in TXI mode1. TXI mode1 can enhance color changes and improve the visibility of SCC suspicious lesions in the pharynx and esophagus, compared to WLI.

Background Endoscopic submucosal dissection (ESD) is technically difficult and requires considerable training. The authors have developed a multi-loop traction device (MLTD), a new traction device that offers easy attachment and detachment. We aimed to evaluate the utility of MLTD in ESD. Methods This ex vivo pilot study was a prospective, block-randomized, comparative study of a porcine stomach model. Twenty-four lesions were assigned to a group that undertook ESD using the MLTD (M-ESD group) and a group that undertook conventional ESD (C-ESD group) to compare the speed of submucosal dissection. In addition, the data of consecutive 10 patients with eleven gastric lesions was collected using electronic medical records to clarify the inaugural clinical outcomes of gastric ESD using MLTD. Results The median (interquartile range) speed of submucosal dissection in the M-ESD and C-ESD groups were 141.5 (60.9–177.6) mm2/min and 35.5 (20.8–52.3) mm2/min, respectively; submucosal dissection was significantly faster in the M-ESD group (p < 0.05). The rate of en bloc resection and R0 resection was 100% in both groups, and there were no perforation in either group. The MLTD attachment time was 2.5 ± 0.9 min and the MLTD extraction time was 1.0 ± 1.1 min. Clinical outcomes of MLTD in gastric ESD were almost the same as those of ex vivo pilot study. Conclusions MLTD increased the speed of submucosal dissection in ESD and was similarly effective when used by expert and trainee endoscopists without perforation. MLTD can potentially ensure a safer and faster ESD.

Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deepleaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL-based models to real-time computer-aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL-based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles.

The cumulative metastasis rate of esophageal squamous cell carcinoma (ESCC) pathologically invading the muscularis mucosae (pT1a-MM), based on lymphovascular invasion (LVI) evaluated by immunohistochemical (IHC) staining is unknown. This retrospective study included patients with endoscopically resected pT1a-MM ESCC. The primary endpoint was the metastasis rate of pT1a-MM based on LVI, evaluated using IHC and additional prophylactic therapy. The secondary endpoint was the identification of independent factors for metastasis based on lesion characteristics. The prognosis was also analyzed considering the impact of head and neck cancer. A total of 104 patients were analyzed, with a median follow-up of 74 months. The positive rate for LVI was 43.3% (45/104). In 33 patients, IHC was not performed at the time of clinical evaluation, 8 of whom exhibited LVI. However, these patients did not exhibit metastasis. The metastasis rates of patients without LVI, those with LVI and additional therapy, and those with LVI without additional therapy were 5.1%, 20.8%, and 0%, respectively. Lesion size ≥ 25 mm was the only independent factor for metastasis in multivariate analysis. The advantage of IHC for determining additional prophylactic therapy is limited for patients with pT1a-MM ESCC.

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