White Light Imaging Research Articles

A handheld device for intra-cavity and ex vivo fluorescence imaging of breast conserving surgery margins with 5-aminolevulinic acid

BackgroundVisualization of cancer during breast conserving surgery (BCS) remains challenging; the BCS reoperation rate is reported to be 20-70% of patients. An urgent clinical need exists for real-time intraoperative visualization of breast carcinomas during BCS. We previously demonstrated the ability of a prototype imaging device to identify breast carcinoma in excised surgical specimens following 5-aminolevulinic acid (5-ALA) administration. However, this prototype device was not designed to image the surgical cavity for remaining carcinoma after the excised lumpectomy specimen is removed. A new handheld fluorescence (FL) imaging prototype device, designed to image both excised specimens and within the surgical cavity, was assessed in a clinical trial to evaluate its clinical utility for first-in-human, real-time intraoperative imaging during index BCS.ResultsThe imaging device combines consumer-grade imaging sensory technology with miniature light-emitting diodes (LEDs) and multiband optical filtering to capture high-resolution white light (WL) and FL digital images and videos. The technology allows for visualization of protoporphyrin IX (PpIX), which fluoresces red when excited by violet-blue light. To date, n=17\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n = 17$$\\end{document} patients have received 20mgkg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20\\frac{\ ext {mg}}{\ extrm{kg}}$$\\end{document} bodyweight (BW) 5-ALA orally 2-4 h before imaging to facilitate the accumulation of PpIX within tumour cells. Tissue types were identified based on their colour appearance. Breast tumours in sectioned lumpectomies appeared red, which contrasted against the green connective tissues and orange-brown adipose tissues. In addition, ductal carcinoma in situ (DCIS) that was missed during intraoperative standard of care was identified at the surgical margin at <1 mm depth. In addition, artifacts due to the surgical drape, illumination, and blood within the surgical cavity were discovered.ConclusionsThis study has demonstrated the detection of a grossly occult positive margin intraoperatively. Artifacts from imaging within the surgical cavity have been identified, and potential mitigations have been proposed.Trial RegistrationClinicalTrials.gov Identifier: NCT01837225 (Trial start date is September 2010. It was registered to ClinicalTrials.gov retrospectively on April 23, 2013, then later updated on April 9, 2020, to reflect the introduction of the new imaging device.)

Standard-definition White-light, High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas: A Multicenter Randomized Controlled Trial.

This study aimed to compare the performance of standard-definition white-light endoscopy (SD-WL), high-definition white-light endoscopy (HD-WL), and high-definition narrow-band imaging (HD-NBI) in detecting colorectal lesions in the Chinese population. This was a multicenter, single-blind, randomized, controlled trial with a non-inferiority design. Patients undergoing endoscopy for physical examination, screening, and surveillance were enrolled from July 2017 to December 2020. The primary outcome measure was the adenoma detection rate (ADR), defined as the proportion of patients with at least one adenoma detected. The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression. Out of 653 eligible patients enrolled, data from 596 patients were analyzed. The ADRs were 34.5% in the SD-WL group, 33.5% in the HD-WL group, and 37.5% in the HD-NBI group (P=0.72). The advanced neoplasm detection rates (ANDRs) in the three arms were 17.1%, 15.5%, and 10.4% (P=0.17). No significant differences were found between the SD group and HD group regarding ADR or ANDR (ADR: 34.5% vs. 35.6%, P=0.79; ANDR: 17.1% vs. 13.0%, P=0.16, respectively). Similar results were observed between the HD-WL group and HD-NBI group (ADR: 33.5% vs. 37.7%, P=0.45; ANDR: 15.5% vs. 10.4%, P=0.18, respectively). In the univariate and multivariate logistic regression analyses, neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL (HD-WL: OR 0.91, P=0.69; HD-NBI: OR 1.15, P=0.80). HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients. It can be concluded that HD-NBI or HD-WL is not superior to SD-WL, but more effective instruction may be needed to guide the selection of different endoscopic methods in the future. Our study's conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources, especially advanced imaging technologies.

Efficacy of texture and color enhancement imaging for the visibility and diagnostic accuracy of non-polypoid colorectal lesions.

A newly launched endoscopy system (EVIS X1, CV-1500; Olympus) is equipped with texture and color enhancement imaging (TXI). We aimed to investigate the efficacy of TXI for the visibility and diagnostic accuracy of non-polypoid colorectal lesions. We examined 100 non-polypoid lesions in 42 patients from the same position, angle, and distance of the view in three modes: white light imaging (WLI), narrow-band imaging (NBI), and TXI. The primary outcome was to compare polyp visibility in the three modes using subjective polyp visibility score and objective color difference values. The secondary outcome was to compare the diagnostic accuracy without magnification. Overall, the visibility score of TXI was significantly higher than that of WLI (3.7 ± 1.1 vs. 3.6 ± 1.1; p = 0.008) and lower than that of NBI (3.7 ± 1.1 vs. 3.8 ± 1.1; p = 0.013). Color difference values of TXI were higher than those of WLI (11.5 ± 6.9 vs. 9.1 ± 5.4; p < 0.001) and lower than those of NBI (11.5 ± 6.9 vs. 13.1 ± 7.7; p = 0.002). No significant differences in TXI and NBI (visibility score: 3.7 ± 1.0 vs. 3.8 ± 1.1; p = 0.833, color difference values: 11.6 ± 7.1 vs. 12.9 ± 8.3; p = 0.099) were observed for neoplastic lesions. Moreover, the diagnostic accuracy of TXI was significantly higher than that of NBI (65.5% vs. 57.6%, p = 0.012) for neoplastic lesions. TXI demonstrated higher visibility than that of WLI and lower than that of NBI. Further investigations are warranted to validate the performance of the TXI mode comprehensively.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Validation of a White Light and Fluorescence Augmented Panoramic Endoscopic Imaging System on a Bimodal Bladder Wall Experimental Model

Background: Fluorescence visualization of pathologies, primarily neoplasms in human internal cavities, is one of the most popular forms of diagnostics during endoscopic examination in medical practice. Currently, visualization can be performed in the augmented reality mode, which allows to observe areas of increased fluorescence directly on top of a usual color image. Another no less informative form of endoscopic visualization in the future can be mapping (creating a mosaic) of the acquired image sequence into a single map covering the area under study. The originality of the present contribution lies in the development of a new 3D bimodal experimental bladder model and its validation as an appropriate phantom for testing the combination of bimodal cystoscopy and image mosaicking. Methods: An original 3D real bladder-based phantom (physical model) including cancer-like fluorescent foci was developed and used to validate the combination of (i) a simultaneous white light and fluorescence cystoscopy imager with augmented reality mode and (ii) an image mosaicking algorithm superimposing both information. Results: Simultaneous registration and real-time visualization of a color image as a reference and a black-and-white fluorescence image with an overlay of the two images was made possible. The panoramic image build allowed to precisely visualize the relative location of the five fluorescent foci along the trajectory of the endoscope tip. Conclusions: The method has broad prospects and opportunities for further developments in bimodal endoscopy instrumentation and automatic image mosaicking.

Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study.

Vocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow-band imaging (NBI) to distinguish benign from malignant VFL. A total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi-instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real-time model performance. A human-machine comparison involving 100 patients and 12 laryngologists assessed the real-world effectiveness of the model. The model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704-0.946). In the human-machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved. Our multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings. 3 Laryngoscope, 134:4321-4328, 2024.

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 10,181 white-light endoscopy images from 2606 patients in an 8:1:1 ratio. Lesions were categorized as early gastric cancer (EGC), advanced gastric cancer (AGC), gastric dysplasia, benign gastric ulcer (BGU), benign polyp, and benign erosion. We assessed the lesion detection and classification model using six-class, cancer versus non-cancer, and neoplasm versus non-neoplasm categories, as well as T-stage estimation in cancer lesions (T1, T2-T4). The lesion detection rate was 95.22% (219/230 patients) on a per-patient basis: 100% for EGC, 97.22% for AGC, 96.49% for dysplasia, 75.00% for BGU, 97.22% for benign polyps, and 80.49% for benign erosion. The six-class category exhibited an accuracy of 73.43%, sensitivity of 80.90%, specificity of 83.32%, positive predictive value (PPV) of 73.68%, and negative predictive value (NPV) of 88.53%. The sensitivity and NPV were 78.62% and 88.57% for the cancer versus non-cancer category, and 83.26% and 89.80% for the neoplasm versus non-neoplasm category, respectively. The T stage estimation model achieved an accuracy of 85.17%, sensitivity of 88.68%, specificity of 79.81%, PPV of 87.04%, and NPV of 82.18%. The novel CNN-based model remarkably detected and classified malignant, premalignant, and benign gastric lesions and accurately estimated gastric cancer T-stages.

Endoscopic evaluation of gastric mucosal alterations in patients with chronic heart failure II-III nyha functional class utilizing advanced endoscopic techniques

Objective: To assess the alterations of gastric mucosa, its pathomorphological and functional characteristics in patients with complete erosions suffering from chronic heart failure II-III NYHA functional class. Materials and Methods: 185 patients with complete erosions comprised the main group, while the control group consisted of 183 patients with incomplete erosions of the stomach. Patients in both groups had chronic heart failure II-III NYHA functional class. All patients underwent esophagogastroduodenoscopy with white light imaging (WLI), narrow-band imaging (NBI), and ZOOM modes, as well as biopsy sampling followed by pathomorphological examination, determination of Helicobacter pylori presence, and endoscopic pH-metry. The diagnosis of chronic heart failure was established based on anamnestic data and the results of instrumental and functional diagnostic methods, including electrocardiography (ECG), echocardiography, Holter monitoring of ECG, stress echocardiography, and coronary angiography. Results: The study utilized the classification of gastric erosive lesions by K. Toljamo (2012), according to which in the main group, mature erosions subtype Ia were detected in 83.8% of cases, while immature extensive erosions subtype Ib were found in 16.2%. In combination with haemorrhagic erosions (type III), complete erosions were detected in 12.6% of cases, while with incomplete erosions of type II, they were found in 11.4%. The combination of complete erosions with erosive prepyloric changes (type IV) was identified in 8.7%. In the control group, gastric erosive lesions were mainly represented by type III (41%) and type IV (38.8%). Based on the analysis of endoscopic changes in the gastric mucosa using WLI, NBI, and ZOOM modes, the most characteristic visual changes were identified for each type and subtype of erosive gastric mucosa alterations. In patients with chronic heart failure II-III NYHA functional class, gastritis was noted in 75.1% of cases in the main group and in 58.5% in the control group. Helicobacter pylori infection of weak degree was determined in 40.5% in the main group and 37.7% in the control group. Evaluation of endoscopic pH-metry showed predominantly hypochlorhydria in both groups - 70.8% in the main group and 48.6% in the control group. Conclusion: By comparing the macroscopic features of various gastric erosive lesions in WLI, NBI, and ZOOM modes according to the classification by K. Toljamo (2012), the most characteristic signs for each type and subtype were identified. High prevalence of gastritis was established among patients with chronic heart failure II-III NYHA functional class.

Comparison of Effective Imaging Modalities for Detecting Gastric Neoplasms: A Randomized 3-Arm Phase II Trial.

The early detection of gastric neoplasms (GNs) leads to favorable treatment outcomes. The latest endoscopic system, EVIS X1, includes third-generation narrow-band imaging (3G-NBI), texture and color enhancement imaging (TXI), and high-definition white-light imaging (WLI). Therefore, this randomized phase II trial aimed to identify the most promising imaging modality for GN detection using 3G-NBI and TXI. Patients with scheduled surveillance endoscopy after a history of esophageal cancer or GN or preoperative endoscopy for known esophageal cancer or GN were randomly assigned to the 3G-NBI, TXI, or WLI groups. Endoscopic observations were performed to detect new GN lesions, and all suspected lesions were biopsied. The primary endpoint was the GN detection rate during primary observation. Secondary endpoints were the rate of missed GNs, early gastric cancer detection rate, and positive predictive value for a GN diagnosis. The decision rule had a higher GN detection rate between 3G-NBI and TXI, outperforming WLI by >1.0%. Finally, 901 patients were enrolled and assigned to the 3G-NBI, TXI, and WLI groups (300, 300, and 301 patients, respectively). GN detection rates in the 3G-NBI, TXI, and WLI groups were 7.3, 5.0, and 5.6%, respectively. The rates of missed GNs were 1.0, 0.7, and 1.0%, the detection rates of early gastric cancer were 5.7, 4.0, and 5.6%, and the positive predictive values for the diagnosis of GN were 36.5, 21.3, and 36.8% in the 3G-NBI, TXI, and WLI groups, respectively. Compared with TXI and WLI, 3G-NBI is a more promising modality for GN detection.

Optical enhancement mode 2 improves the detection rate of gastric neoplastic lesion in high-risk populations: A multicenter randomized controlled clinical study.

Detection of early neoplastic lesions is crucial for improving the survival rates of patients with gastric cancer. Optical enhancement mode 2 is a new image-enhanced endoscopic technique that offers bright images and can improve the visibility of neoplastic lesions. This study aimed to compare the detection of neoplastic lesions with optical enhancement mode 2 and white-light imaging (WLI) in a high-risk population. In this prospective multicenter randomized controlled trial, patients were randomly assigned to optical enhancement mode 2 or WLI groups. Detection of suspicious neoplastic lesions during the examinations was recorded, and pathological diagnoses served as the gold standard. A total of 1211 and 1219 individuals were included in the optical enhancement mode 2 and WLI groups, respectively. The detection rate of neoplastic lesions was significantly higher in the optical enhancement mode 2 group (5.1% vs. 1.9%; risk ratio, 2.656 [95% confidence interval, 1.630-4.330]; p<0.001). The detection rate of neoplastic lesions with an atrophic gastritis background was significantly higher in the optical enhancement mode 2 group (8.6% vs. 2.6%, p<0.001). The optical enhancementmode2 group also had a higher detection rate among endoscopists with different experiences. Optical enhancement mode 2 was more effective than WLI for detecting neoplastic lesions in the stomach, and can serve as a new method for screening early gastric cancer in clinical practice. United States National Library of Medicine (https://www. gov), ID: NCT040720521.

Recent advancements in image-enhanced endoscopy in the pancreatobiliary field.

Image-enhanced endoscopy (IEE) has advanced gastrointestinal disease diagnosis and treatment. Traditional white-light imaging has limitations in detecting all gastrointestinal diseases, prompting the development of IEE. In this review, we explore the utility of IEE, including texture and color enhancement imaging and red dichromatic imaging, in pancreatobiliary (PB) diseases. IEE includes methods such as chromoendoscopy, optical-digital, and digital methods. Chromoendoscopy, using dyes such as indigo carmine, aids in delineating lesions and structures, including pancreato-/cholangio-jejunal anastomoses. Optical-digital methods such as narrow-band imaging enhance mucosal details and vessel patterns, aiding in ampullary tumor evaluation and peroral cholangioscopy. Moreover, red dichromatic imaging with its specific color allocation, improves the visibility of thick blood vessels in deeper tissues and enhances bleeding points with different colors and see-through effects, proving beneficial in managing bleeding complications post-endoscopic sphincterotomy. Color enhancement imaging, a novel digital method, enhances tissue texture, brightness, and color, improving visualization of PB structures, such as PB orifices, anastomotic sites, ampullary tumors, and intraductal PB lesions. Advancements in IEE hold substantial potential in improving the accuracy of PB disease diagnosis and treatment. These innovative techniques offer advantages paving the way for enhanced clinical management of PB diseases. Further research is warranted to establish their standard clinical utility and explore new frontiers in PB disease management.

Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging

ObjectivesThis study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. MethodsWe collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. ResultsThe CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. ConclusionsThe CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.

Objective Comparison of White Light and Narrow-Band Imaging for Detecting Scars, Sulci and Nodules.

Narrow-band imaging (NBI) can improve detection of lesions in the aerodigestive tract. However, its role in benign lesions of the larynx is unclear. This study aims to determine whether NBI improves the detection of scars, sulci, and nodules compared to panchromatic lighting using objective image analysis. In total, 120 vocal folds (VFs) were analyzed with and without NBI (21 normal, 15 scars, 16 sulci, and 45 nodules). Each VF image had anterior, middle, and posterior thirds analyzed for brightness using an area morphometry software (Optimas 5.1a). The middle-third with the lesion was analyzed against surrounding VF segments for average and standard deviation (SD) in absolute grayscale. The use of panchromatic light resulted in greater illumination and grayscale values than NBI. All lesions tended to be in the mid-membranous fold. Under panchromatic light, change in brightness when comparing anterior versus middle (A-M) was +6.1% for normal, versus 6.5%, 8.1%, and 7.1% for sulci, nodules, and scars, respectively. Under NBI, they were 9.0% (normal), 12.3% (sulci), 13.7% (nodules), and 13.1% (scars). A greater SD of luminescence was observed at pathology sites (p < 0.05) when using NBI. The change in absolute grayscale at all lesion sites was greater when using NBI than when using panchromatic light (p < 0.05). NBI significantly enhanced the area of pathology in patients with nodules, sulci, and scars. Greater SD values in grayscale at pathologic sites were observed compared at normal sites. Thus, NBI may improve the detection of phonotraumatic lesions compared to panchromatic light. N/A Laryngoscope, 134:4066-4070, 2024.

Visibility of esophageal squamous cell carcinoma under iodine staining on texture and color enhancement imaging

AbstractObjectiveIodine staining on white light imaging (WLI) is the gold standard for detecting and demarcating esophageal squamous cell carcinoma (ESCC). We examined the effects of texture and color enhancement imaging (TXI) on improving the endoscopic visibility of ESCC under iodine staining.MethodsTwenty ESCC lesions that underwent endoscopic submucosal dissection were retrospectively included. The color difference between ESCC and the surrounding mucosa (ΔEe) on WLI, TXI, and narrow‐band imaging was assessed, and ΔEe under 1% iodine staining on WLI and TXI. Furthermore, the visibility grade determined by endoscopists was evaluated on each imaging.ResultThe median ΔEe was greater on TXI than on WLI (14.53 vs. 10.71, respectively; p &lt; 0.005). Moreover, the median ΔEe on TXI under iodine staining was greater than the median ΔEe on TXI and narrow‐band imaging (39.20 vs. 14.53 vs. 16.42, respectively; p &lt; 0.005 for both). A positive correlation in ΔEe under iodine staining was found between TXI and WLI (correlation coefficient = 0.61, p &lt; 0.01). Moreover, ΔEe under iodine staining on TXI in each lesion was greater than the corresponding ΔEe on WLI. The visibility grade assessed by endoscopists on TXI was also significantly greater than that on WLI under iodine staining (p &lt; 0.01).ConclusionsThe visibility of ESCC after iodine staining was greater on TXI than on WLI.

MatrixCraCS: Automated tracking of matrix crack development in GFRP laminates undergoing large tensile strains

A novel image processing method for tracking development of matrix cracks in glass-fibre reinforced polymer (GFRP) laminates is presented. Images are acquired in a quasi-static tension test using transillumination white light imaging. These images show changes occurring due to developing damage. The method, called MatrixCraCS, tracks and quantifies matrix cracks while compensating for large specimen deformation and stitching threads, which obscure crack development and seriously impact crack detection. Matrix cracks are tracked via a sequential image difference and morphological filtering is used to detect cracks. MatrixCraCS is verified against synthetic images and shows an error of less than 7% in the quantified length of cracks and 0% detection failures. MatrixCraCS is validated against experimental results for a [±452°902°]S GFRP laminate, where microscopic analysis shows 5.92% error in crack detection. Error relative to manual crack tracking are quantified for detection failures and false positives as 3.3% and 1.57%, respectively. It is found that the method accurately quantifies cracks in GFRP laminates subject to large deformation and is robust against noise.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning

PurposeInformative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination.MethodsSeveral deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model.ResultsResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials.ConclusionThe deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

Fluorescent Imaging Using Indocyanine Green for Intraoperative Detection of Endometriosis [ID 2683620

INTRODUCTION: Endometriosis is characterized by the presence of endometrial-like tissue outside of the uterus. Definitive diagnosis is made by laparoscopic visualization with confirmatory histopathologic biopsy. In some cases, lesions can be ambiguous or difficult to visualize with traditional white light (WL)-illuminated laparoscopy. The study objective is to evaluate whether the use of indocyanine green (ICG) aids in identification and removal of clear or ambiguous endometriosis lesions, not visualized under traditional WL imaging. METHODS: This is a retrospective chart review of patients with symptoms suspicious for endometriosis where ICG is used during laparoscopy to assist in identification of suspected endometriosis lesions. Data collection includes patient demographics, medical history, surgical history, preoperative symptoms, and localization of lesions. Data will be collected from hospital electronic health record system and from a private practice. RESULTS: In total, 211 suspected lesions were excised, of which 115 had histologically proven endometriosis. 196 lesions were detected using ICG and 112 were confirmed to be endometriosis. These results were statistically significant (P=.024) with a sensitivity of 97.4% and a negative predictive value of 80%, demonstrating that ICG can significantly improve the intraoperative detection of endometriosis lesions. Research is ongoing with more data being collected. Our goal is that the additional data will further strengthen the power of our study. CONCLUSION: Our study shows that ICG fluorescence significantly improves the intraoperative detection and resection of ambiguous or clear endometriosis lesions and improves histopathologic diagnosis when compared to WL imaging. Indocyanine green has the potential to increase the detection of endometriosis and improve patient outcomes.

Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets.

Detailed superiority of CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for polyp detection/diagnosis, compared to endoscopists is not well examined. We examined endoscopist's ability using movie sets of colorectal lesions which were detected and diagnosed by CAD EYE accurately. Consecutive lesions of ≤10 mm were examined live by CAD EYE from March-June 2022 in our institution. Short unique movie sets of each lesion with and without CAD EYE were recorded simultaneously using two recorders for detection under white light imaging (WLI) and linked color imaging (LCI) and diagnosis under blue laser/light imaging (BLI). Excluding inappropriate movies, 100 lesions detected and diagnosed with CAD EYE accurately were evaluated. Movies without CAD EYE were evaluated first by three trainees and three experts. Subsequently, movies with CAD EYE were examined. The rates of accurate detection and diagnosis were evaluated for both movie sets. Among 100 lesions (mean size: 4.7±2.6 mm; 67 neoplastic/33 hyperplastic), mean accurate detection rates of movies without or with CAD EYE were 78.7%/96.7% under WLI (p<0.01) and 91.3%/97.3% under LCI (p<0.01) for trainees and 85.3%/99.0% under WLI (p<0.01) and 92.6%/99.3% under LCI (p<0.01) for experts. Mean accurate diagnosis rates of movies without or with CAD EYE for BLI were 85.3%/100% for trainees (p<0.01) and 92.3%/100% for experts (p<0.01), respectively. The significant risk factors of not-detected lesions for trainees were right-sided, hyperplastic, not-reddish, in the corner, halation, and inadequate bowel preparation. Unique movie sets with and without CAD EYE could suggest it's efficacy for lesion detection/diagnosis.

Linked color imaging improves colorectal lesion detection especially for low performance endoscopists: An international trial in Asia.

Linked color imaging (LCI) is an image-enhanced endoscopy technique that accentuates the color difference between red and white, potentially improving the adenoma detection rate (ADR). However, it remains unclear whether LCI performance in detecting colorectal lesions differs based on endoscopists' experience levels. We aimed to evaluate the differences in LCI efficacy based on the experience levels of endoscopists by conducting an exploratory analysis. In this post hoc analysis of an international randomized controlled trial comparing the detection of adenoma and other lesions using colonoscopy with LCI and high-definition white light imaging (WLI), we included patients from 11 institutions across four countries/regions: Japan, Thailand, Taiwan, and Singapore. We retrospectively reviewed differences in the lesion detection of LCI according to endoscopists' colonoscopy history or ADR. We included 1692 and 1138 patients who underwent colonoscopies performed by 54 experts (experience of ≥5000 colonoscopies) and by 43 non-experts (experience of <5000 colonoscopies), respectively. Both expert and non-expert groups showed a significant improvement in ADR with LCI compared to WLI (expert, 61.7% vs 46.4%; P<0.001; non-expert, 56.6% vs 46.4%; P<0.001). LCI had no effect on sessile serrated lesion detection rate in non-experts (3.1% vs 2.5%; P=0.518). LCI significantly improved detection rates in endoscopists with relatively low detection performance, defined as an ADR<50%. This exploratory study analyzed data from a previous trial and revealed that LCI is useful for both experts and non-experts and is even more beneficial for endoscopists with relatively low detection performance using WLI.

Estimating the lateral speed of a fast shock driven by a coronal mass ejection at the location of solar radio emissions

Fast coronal mass ejections (CMEs) can drive shock waves capable of accelerating electrons to high energies. These shock-accelerated electrons act as sources of electromagnetic radiation, often in the form of solar radio bursts. Recent findings suggest that radio imaging of solar radio bursts can provide a means to estimate the lateral expansion of CMEs and associated shocks in the low corona. Our aim is to estimate the expansion speed of a CME-driven shock at the locations of radio emission using 3D reconstructions of the shock wave from multiple viewpoints. In this study, we estimated the 3D location of radio emission using radio imaging from the Nançay Radioheliograph and the 3D location of a CME-driven shock. The 3D shock was reconstructed using white-light and extreme ultraviolet images of the CME from the Solar Terrestrial Relations Observatory, Solar Dynamics Observatory, and the Solar and Heliospheric Observatory. The lateral expansion speed of the CME-driven shock at the electron acceleration locations was then estimated using the approximate 3D locations of the radio emission on the surface of the shock. The radio bursts associated with the CME were found to reside at the flank of the expanding CME-driven shock. We identified two prominent radio sources at two different locations and found that the lateral speed of the shock was between $ $ and $ second $ at these locations. Such a high speed during the early stages of the eruption already indicates the presence of a fast shock in the low corona. We also found a larger ratio between the radial and lateral expansion speed compared to values obtained higher up in the corona. We estimated for the first time the 3D expansion speed of a CME-driven shock at the location of the accompanying radio emission. The high shock speed obtained is indicative of a fast acceleration during the initial stage of the eruption. This acceleration leading to lateral speeds in the range of $ second $ is most likely one of the key parameters contributing to the presence of metric radio emissions, such as type II radio bursts.