Narrow Band Imaging

BackgroundMagnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI.MethodsA total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.ResultsThe sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts.ConclusionsOur CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.

BackgroundIn the early stage esophageal cancer, changes in the mucosa are subtle and pass unnoticed in endoscopic examinations using white light. To increase sensitivity, chromoscopy with Lugol's solution has been used. Technological advancements have led to the emergence of virtual methods of endoscopic chromoscopy, including narrow band imaging (NBI). NBI enhances the relief of the mucosa and the underlying vascular pattern, providing greater convenience without the risks inherent to the use of vital dye. The purpose of this systematic review and meta-analysis was to evaluate the ability of NBI to diagnose squamous cell carcinoma of the esophagus and to compare it to chromoscopy with Lugol's solution.MethodsThis systematic review included all studies comparing the diagnostic accuracy of NBI and Lugol chromoendoscopy performed to identify high-grade dysplasia and/or squamous cell carcinoma in the esophagus. In the meta-analysis, we calculated and demonstrated sensitivity, specificity, and positive and negative likelihood values in forest plots. We also determined summary receiver operating characteristic (sROC) curves and estimates of the areas under the curves for both per-patient and per-lesion analysis.ResultsThe initial search identified 7079 articles. Of these, 18 studies were included in the systematic review and 12 were used in the meta-analysis, for a total of 1911 patients. In per-patient and per-lesion analysis, the sensitivity, specificity, and positive and negative likelihood values for Lugol chromoendoscopy were 92% and 98, 82 and 37%, 5.42 and 1.4, and 0.13 and 0.39, respectively, and for NBI were 88 and 94%, 88 and 65%, 8.32 and 2.62, and 0.16 and 0.12, respectively. There was a statistically significant difference in only specificity values, in which case NBI was superior to Lugol chromoendoscopy in both analyses. In the per-patient analysis, the area under the sROC curve for Lugol chromoendoscopy was 0.9559. In the case of NBI, this value was 0.9611; in the per-lesion analysis, this number was 0.9685 and 0.9587, respectively.ConclusionsNBI was adequate in evaluating the esophagus in order to diagnose high-grade dysplasia and squamous cell carcinoma. In the differentiation of those disorders from other esophageal mucosa alterations, the NBI was shown to be superior than Lugol.

We aimed to develop a computer-aided diagnostic system (CAD) for predicting colorectal polyp histology using deep-learning technology and to validate its performance. Near-focus narrow-band imaging (NBI) pictures of colorectal polyps were retrieved from the database of our institution. Of these, 12480 image patches of 624 polyps were used as a training set to develop the CAD. The CAD performance was validated with two test datasets of 545 polyps. Polyps were classified into three histological groups: serrated polyp (SP), benign adenoma (BA)/mucosal or superficial submucosal cancer (MSMC), and deep submucosal cancer (DSMC). The overall kappa value measuring the agreement between the true polyp histology and the expected histology by the CAD was 0.614-0.642, which was higher than that of trainees (n = 6, endoscopists with experience of 100 NBI colonoscopies in <6 months; 0.368-0.401) and almost comparable with that of the experts (n = 3, endoscopists with experience of 2,500 NBI colonoscopies in ≥5 years) (0.649-0.735). The areas under the receiver operating curves for CAD were 0.93-0.95, 0.86-0.89, and 0.89-0.91 for SP, BA/MSMC, and DSMC, respectively. The overall diagnostic accuracy of the CAD was 81.3-82.4%, which was significantly higher than that of the trainees (63.8-71.8%, P < 0.01) and comparable with that of experts (82.4-87.3%). The kappa value and diagnostic accuracies of the trainees improved with CAD assistance: that is, the kappa value increased from 0.368 to 0.655, and the overall diagnostic accuracy increased from 63.8-71.8% to 82.7-84.2%. CAD using a deep-learning model can accurately assess polyp histology and may facilitate the diagnosis of colorectal polyps by endoscopists.

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present subtle mucosal changes that are difficult to identify using standard White Light Imaging (WLI), and hardware-based Narrow Band Imaging (NBI) is not universally available. In this study, we propose a novel image processing algorithm termed the Spectrum-Aided Vision Enhancer (SAVE) to address these challenges in computer-aided diagnosis (CAD). Leveraging hyperspectral data principles, SAVE transforms standard WLI endoscopic images into enhanced, NBI-like representations, significantly improving mucosal contrast and lesion visibility without requiring additional hardware. To validate the efficacy of this approach for medical image analysis, we utilized a dataset of Squamous Cell Carcinoma (SCC) and dysplasia. We conducted a comprehensive comparative analysis using five state-of-the-art deep learning models: YOLOv8, InceptionV3, Inception-ResNet-V2, ConvNeXt-V2, and MobileNetV2. Experimental results demonstrate that models trained on SAVE-enhanced images significantly outperform those trained on traditional WLI in both classification and detection tasks. This study presents a cost-effective, software-driven solution that integrates advanced image processing with deep learning, offering a robust tool for the automated screening of esophageal malignancies.