Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer.

BACKGROUND AND AIMS: Endoscopic treatment is recommended for low-grade dysplasia (LGD), high-grade dysplasia (HGD), and colorectal cancer (CRC) with submucosal (SM) invasion <1000 µm. However, diagnosis of invasion depth requires experience and is often difficult. This study developed and evaluated a novel computer-aided diagnosis (CAD) system to determine whether endoscopic treatment is appropriate for colorectal lesions using only white-light endoscopy (WLE). METHODS: We extracted 3442 images from 1035 consecutive colorectal lesions (105 LGDs, 377 HGDs, 107 CRCs with SM <1000 µm, 146 CRCs with SM ≥1000 µm, and 300 advanced CRCs). All images were WLE, nonmagnified, and nonstained. We developed a novel CAD system using 2751 images; the remaining 691 images were evaluated by the CAD system as a test set. The capability of the CAD system to distinguish endoscopically treatable lesions and untreatable lesions was assessed and compared with the results from 2 trainees and 2 experts. RESULTS: The CAD system distinguished endoscopically treatable from untreatable lesions with 96.7% sensitivity, 75.0% specificity, and 90.3% accuracy. These values were significantly higher than those from trainees (92.1%, 67.6%, and 84.9%; P < .01, <.01, and <.01, respectively) and were comparable with those from experts (96.5%, 72.5%, and 89.4%, respectively). Trainees assisted by the CAD system demonstrated a diagnostic capability comparable with that of experts. CONCLUSIONS: The CAD system had good diagnostic capability for making treatment decisions for colorectal lesions. This system may enable a more convenient and accurate diagnosis using only WLE.

Identification of Appendicitis Using Ultrasound with the Aid of Machine Learning.

Background: Diagnosing pediatric appendicitis by ultrasonography (US) is difficult because US requires significant training and skill. We evaluated whether artificial intelligence (AI) can augment US. Materials and Methods: Among 70 abdominal ultrasound videos containing 85-347 images each, 50 were used to train the AI neural network. Each video was categorized based on the detection percentage and percent accuracy: most (>50%), partial (10-50%), and none (<10%). Test 1 involved verification of appendix detection by AI using the remaining 20 videos. Test 2 involved the evaluation of the effect of AI utilization on pediatricians. Results: From 50 videos, 6914 images were used to train the AI network. In test 1, 3 pediatric surgeons judged 10 (50.0%), 4 (20.0%), and 6 (30.0%) videos as "most," "partial," and "none," respectively, regarding the detection percentage; 7 (35.0%), 7 (35.0%), and 6 (30.0%) videos were judged, respectively, concerning the percent accuracy. Five (83.3%) of six test videos with a scan area depth of 8 cm were judged as "none" for both detection and accuracy. In test 2, six videos were also judged as "none" for both categories, showing a negative effect on the participants (5 pediatric residents and 5 pediatric intensive-emergency fellows), but the other categories showed little negative effect. Conclusions: Appendicitis in a shallow US scan area can be easily identified with AI support. Even with the detection of a partial appendicitis shadow, AI is still helpful. However, if AI does not detect appendicitis at all, examiners may be negatively affected.

Metabolic responses and arginine kinase expression under hypoxic stress of the kuruma prawn Marsupenaeus japonicus.

In response to hypoxia at PO(2) 1.3-1.7 mg/L for 6 h, the kuruma prawn Marsupenaeus (Penaeus) japonicus showed a dramatic decrease in phosphoarginine storage in muscle, with normal levels restored during 4-h post-hypoxic recovery. Large stores of muscle glycogen only decreased between 4 and 6 h during hypoxia, but greatly diminished during recovery. Muscle ATP levels and energy charge decreased only slightly under hypoxia. Lactate levels increased slightly during hypoxia and promptly returned to control levels during recovery. These data indicate that phosphoarginine works in muscle as an ATP buffer during hypoxia and glycogen is utilized as an energy source during recovery. Under hypoxia, up- and down-regulated proteins were identified after 2D electrophoresis and partial sequences were obtained after protease digestion. Fructose bisphosphate aldolase was down-regulated during hypoxia, suggesting the suppression of glycolysis under hypoxia. Several partial sequences from three protein spots up-regulated under hypoxia were all assigned to arginine kinase, suggesting the existence of several isoforms of arginine kinase in the muscle of M. japonicus. This arginine kinase up-regulation under hypoxia may indicate a provision for oxygen re-supply after anaerobiosis. This is consistent with the prompt replenishment of phosphoarginine stores during recovery from hypoxia.