A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

BACKGROUND: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. METHODS: ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. RESULTS: The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P<0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P<0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P<0.001). CONCLUSIONS: ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study.

BACKGROUND AND AIMS: The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS: This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS: A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS: AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Development and Validation of a Deep Learning-Based Histologic Diagnosis System for Diagnosing Colorectal Sessile Serrated Lesions.

OBJECTIVES: The histopathologic diagnosis of colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) is of low consistency among pathologists. This study aimed to develop and validate a deep learning (DL)-based logical anthropomorphic pathology diagnostic system (LA-SSLD) for the differential diagnosis of colorectal SSL and HP. METHODS: The diagnosis framework of the LA-SSLD system was constructed according to the current guidelines and consisted of 4 DL models. Deep convolutional neural network (DCNN) 1 was the mucosal layer segmentation model, DCNN 2 was the muscularis mucosa segmentation model, DCNN 3 was the glandular lumen segmentation model, and DCNN 4 was the glandular lumen classification (aberrant or regular) model. A total of 175 HP and 127 SSL sections were collected from Renmin Hospital of Wuhan University during November 2016 to November 2022. The performance of the LA-SSLD system was compared to 11 pathologists with different qualifications through the human-machine contest. RESULTS: The Dice scores of DCNNs 1, 2, and 3 were 93.66%, 58.38%, and 74.04%, respectively. The accuracy of DCNN 4 was 92.72%. In the human-machine contest, the accuracy, sensitivity, and specificity of the LA-SSLD system were 85.71%, 86.36%, and 85.00%, respectively. In comparison with experts (pathologist D: accuracy 83.33%, sensitivity 90.91%, specificity 75.00%; pathologist E: accuracy 85.71%, sensitivity 90.91%, specificity 80.00%), LA-SSLD achieved expert-level accuracy and outperformed all the senior and junior pathologists. CONCLUSIONS: This study proposed a logical anthropomorphic diagnostic system for the differential diagnosis of colorectal SSL and HP. The diagnostic performance of the system is comparable to that of experts and has the potential to become a powerful diagnostic tool for SSL in the future. It is worth mentioning that a logical anthropomorphic system can achieve expert-level accuracy with fewer samples, providing potential ideas for the development of other artificial intelligence models.

Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data.

OBJECTIVES: Accurate endoscopic optical prediction of the depth of cancer invasion is critical for guiding an optimal treatment approach of large sessile colorectal polyps but was hindered by insufficient endoscopists expertise and inter-observer variability. We aimed to construct a clinically applicable artificial intelligence (AI) system for the identification of presence of cancer invasion in large sessile colorectal polyps. METHODS: A deep learning-based colorectal cancer invasion calculation (CCIC) system was constructed. Multi-modal data including clinical information, white light (WL) and image-enhanced endoscopy (IEE) were included for training. The system was trained using 339 lesions and tested on 198 lesions across three hospitals. Man-machine contest, reader study and video validation were further conducted to evaluate the performance of CCIC. RESULTS: The overall accuracy of CCIC system using image and video validation was 90.4% and 89.7%, respectively. In comparison with 14 endoscopists, the accuracy of CCIC was comparable with expert endoscopists but superior to all the participating senior and junior endoscopists in both image and video validation set. With CCIC augmentation, the average accuracy of junior endoscopists improved significantly from 75.4% to 85.3% (P = 0.002). CONCLUSIONS: This deep learning-based CCIC system may play an important role in predicting the depth of cancer invasion in colorectal polyps, thus determining treatment strategies for these large sessile colorectal polyps.

Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study.

Background: Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient survival. However, most previous computer-aided-diagnosis (CAD) systems did not concretize or explain diagnostic theories. We aimed to develop a logical anthropomorphic artificial intelligence (AI) diagnostic system named ENDOANGEL-LA (logical anthropomorphic) for EGCs under magnifying image enhanced endoscopy (M-IEE). Methods: We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared. Findings: Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model. Interpretation: We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs. Funding: This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).

Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos).

BACKGROUND AND AIMS: We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition. METHODS: This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs. RESULTS: One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively). CONCLUSIONS: The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.

Purification, characterization, and identification of 3-hydroxy-4-methoxy benzal acrolein-an intermediate of synthesizing advantame.

Advantame is a novel sweetener, and 3-hydroxy-4-methoxy benzal acrolein is an important intermediate to synthesize the sweetener. The aim of this study was to evaluate a new low-cost method to purify 3-hydroxy-4-methoxy benzal acrolein, and the crude intermediate was used as raw material. The intermediate was purified using a low-pressure column chromatography with a C18 column, using a methanol-water (6:4, v/v) at a flow rate of 6.0 ml/min, and the loading amount of the crude intermediate in solution was 10.0 mg in total. A method for the analysis of 3-hydroxy-4-methoxy benzal acrolein was established using high-performance liquid chromatography (HPLC). This method was validated in terms of its linearity, repeatability, accuracy, detection limit, and quantitation limit. The calibration curves of 3-hydroxy-4-methoxy benzal acrolein were linear (r > .999) over a wide concentration range of 0.005-0.08 mg/ml, by comparing the ratio of signal to noise, and the detection limit was 5.0 ng/ml and the quantification limit was 15.0 ng/ml. Good repeatability was obtained (RSD < 2%, n = 6), and the recoveries calculated using mixed sample previously quantified ranged from 94.5% to 106.37%. As long as, this method has been successfully applied to the analysis of 3-hydroxy-4-methoxy benzal acrolein; therefore, the method can be put into practical use during the industrial synthesis and real-time detection of the intermediate.

Alpha-linolenic acid-loaded oil/water microemulsion: Effects of phase behaviour simulation and environmental stress on phase stabilizing and anti-oxidation capacity.

α-Linolenic acid (ALA)-loaded microemulsion (ME) was prepared from isoamyl acetate, polyoxyethylene ether 35 (EL-35), ethanol and water. The dynamic phase behaviour was simulated using dissipative particle dynamics (DPD), which showed that spherical ME was formed at water/oil ratios of 1:9 and 9:1, while a lamellar structure with distinctive water-course and oil layer appeared at ratios of 3:7, 5:5, and 7:3. Phase stabilizing and anti-oxidation effect of environmental stresses on ALA-loaded microemulsion were investigated. Results showed that the ME region was large and had good environmental tolerance. Subsequently, the investigation of anti-oxidation stability revealed that more than 60% ALA of ALA-loaded ME could be protected from oxidation under environmental stresses. Furthermore, ALA-loaded ME was applied in aqueous-based foods. The transparency, precipitate, stratification and phase separation were used to evaluate influence of ME on product properties, confirming great feasibility and stability of ALA-loaded ME for practical applications.

Enhancement of the solubility and antioxidant capacity of α-linolenic acid using an oil in water microemulsion.

The applications of α-linolenic acid (ALA) in the food industry are restricted due to its poor water solubility and antioxidant stability. This study concentrates on developing an ALA-loaded microemulsion (ALA-ME) to enhance its solubility and antioxidant capacity. The formulation of the microemulsion was investigated based on pseudoternary phase diagrams. The ALA-ME was characterized by using electrical conductivity, viscosity and transmission electron microscopy (TEM). The microstructure of the ALA-ME was probed using nuclear magnetic resonance (1H-NMR). The results proved that ALA-ME consisted of spheroidal droplets with 20-40 nm diameter. A structural transformation from water in oil (W/O) to oil in water (O/W) occurred, as seen from the electrical conductivity determination. The 1H-NMR results revealed a transition of the ALA position encapsulated from the core area of the microemulsion to the lipophilic layer of the surfactant. Furthermore, two microstructural models of ALA-ME were proposed. The antioxidant evaluation demonstrated that the ALA antioxidant capacity in microemulsions was enhanced to about 80% compared with that of ALA in oil solution.

Quantitative assessment of bioactive compounds and the antioxidant activity of 15 jujube cultivars.

Fifteen jujube cultivars late in their maturation were analysed in the red stage for bioactive compounds; including total phenolics (bound/free), total flavonoids, total polysaccharides, ascorbic acid, total triterpenes, proanthocyanidins and cyclic adenosine monophosphate (cAMP). The antioxidant activity was evaluated using the 2,2-diphenyl-1-picrylhydracyl (DPPH) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonicacid) (ABTS(+)) scavenging methods and the ferric reducing antioxidant power (FRAP) assay. The Order Performance by Similarity to Ideal Solution method (TOPSIS) was employed to evaluate the nutrition of different jujube cultivars based on their bioactive compounds. The results indicated that the contents of bioactive compounds and antioxidant capacities vary between different jujube cultivars. Correlation analysis revealed that ascorbic acid, polyphenols and proanthocyanidins were the 3 main components responsible for the antioxidant activity of jujubes. TOPSIS analysis indicated that Zyzyphus jujube cv. Nanjingyazao ranks the highest of the 15 jujube fruits with regards to nutritional value.