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Transitions between the Zeeman substates of Na(3p 2P1/2) and Na(3p 2P3/2) in collision with Na(3s 2S1/2) in thermal energy region are investigated by the close-coupling method. Rotational and spin–orbit couplings are taken into consideration, and potential energy functions generated by a quantum chemical calculation are employed. The cross sections strongly depend on the magnetic quantum numbers of the initial and final states. This dependence qualitatively coincides with an experimental observation in the presence of a weak magnetic field, although the magnetic field is not considered in the present calculation. Total cross section of the transition Na(2P3/2)→Na(2P1/2) is calculated to be 2.7×102 Å2 in good agreement with experiment. The following two mechanisms are found to dominantly contribute to the total cross section: (1) Transitions among the molecular states of Na2, 3Π2u↔3Π1u and 3Π1u↔3Π0u, induced by the spin–rotation coupling, and (2) the transition, 1Σ+0g↔1Π1g induced by the Coriolis coupling. [ABSTRACT FROM AUTHOR]

Background: The Critical View of Safety (CVS) was proposed in 1995 to prevent bile duct injury during laparoscopic cholecystectomy (LC). The achievement of CVS was evaluated subjectively. This study aimed to develop an artificial intelligence (AI) system to evaluate CVS scores in LC. Materials and methods: AI software was developed to evaluate the achievement of CVS using an algorithm for image classification based on a deep convolutional neural network. Short clips of hepatocystic triangle dissection were converted from 72 LC videos, and 23,793 images were labeled for training data. The learning models were examined using metrics commonly used in machine learning. Results: The mean values of precision, recall, F-measure, specificity, and overall accuracy for all the criteria of the best model were 0.971, 0.737, 0.832, 0.966, and 0.834, respectively. It took approximately 6 fps to obtain scores for a single image. Conclusions: Using the AI system, we successfully evaluated the achievement of the CVS criteria using still images and videos of hepatocystic triangle dissection in LC. This encourages surgeons to be aware of CVS and is expected to improve surgical safety. [ABSTRACT FROM AUTHOR]

Background: Attention to anatomical landmarks in the appropriate surgical phase is important to prevent bile duct injury (BDI) during laparoscopic cholecystectomy (LC). Therefore, we created a cross-AI system that works with two different AI algorithms simultaneously, landmark detection and phase recognition. We assessed whether landmark detection was activated in the appropriate phase by phase recognition during LC and the potential contribution of the cross-AI system in preventing BDI through a clinical feasibility study (J-SUMMIT-C-02). Methods: A prototype was designed to display landmarks during the preparation phase and Calot's triangle dissection. A prospective clinical feasibility study using the cross-AI system was performed in 20 LC cases. The primary endpoint of this study was the appropriateness of the detection timing of landmarks, which was assessed by an external evaluation committee (EEC). The secondary endpoint was the correctness of landmark detection and the contribution of cross-AI in preventing BDI, which were assessed based on the annotation and 4-point rubric questionnaire. Results: Cross-AI-detected landmarks in 92% of the phases where the EEC considered landmarks necessary. In the questionnaire, each landmark detected by AI had high accuracy, especially the landmarks of the common bile duct and cystic duct, which were assessed at 3.78 and 3.67, respectively. In addition, the contribution to preventing BDI was relatively high at 3.65. Conclusions: The cross-AI system provided landmark detection at appropriate situations. The surgeons who previewed the model suggested that the landmark information provided by the cross-AI system may be effective in preventing BDI. Therefore, it is suggested that our system could help prevent BDI in practice. Trial registration University Hospital Medical Information Network Research Center Clinical Trial Registration System (UMIN000045731). [ABSTRACT FROM AUTHOR]

Background: According to the National Clinical Database of Japan, the incidence of bile duct injury (BDI) during laparoscopic cholecystectomy has hovered around 0.4% for the last 10 years and has not declined. On the other hand, it has been found that about 60% of BDI occurrences are due to misidentifying anatomical landmarks. However, the authors developed an artificial intelligence (AI) system that gave intraoperative data to recognize the extrahepatic bile duct (EHBD), cystic duct (CD), inferior border of liver S4 (S4), and Rouviere sulcus (RS). The purpose of this research was to evaluate how the AI system affects landmark identification. Methods: We prepared a 20-s intraoperative video before the serosal incision of Calot's triangle dissection and created a short video with landmarks overwritten by AI. The landmarks were defined as landmark (LM)-EHBD, LM-CD, LM-RS, and LM-S4. Four beginners and four experts were recruited as subjects. After viewing a 20-s intraoperative video, subjects annotated the LM-EHBD and LM-CD. Then, a short video is shown with the AI overwriting landmark instructions; if there is a change in each perspective, the annotation is changed. The subjects answered a three-point scale questionnaire to clarify whether the AI teaching data advanced their confidence in verifying the LM-RS and LM-S4. Four external evaluation committee members investigated the clinical importance. Results: In 43 of 160 (26.9%) images, the subjects transformed their annotations. Annotation changes were primarily observed in the gallbladder line of the LM-EHBD and LM-CD, and 70% of these shifts were considered safer changes. The AI-based teaching data encouraged both beginners and experts to affirm the LM-RS and LM-S4. Conclusion: The AI system provided significant awareness to beginners and experts and prompted them to identify anatomical landmarks linked to reducing BDI. [ABSTRACT FROM AUTHOR]