Precise highlighting of the pancreas by semantic segmentation during robot-assisted gastrectomy: visual assistance with artificial intelligence for surgeons.

BACKGROUND: A postoperative pancreatic fistula (POPF) is a critical complication of radical gastrectomy for gastric cancer, mainly because surgeons occasionally misrecognize the pancreas and fat during lymphadenectomy. Therefore, this study aimed to develop an artificial intelligence (AI) system capable of identifying and highlighting the pancreas during robot-assisted gastrectomy. METHODS: A pancreas recognition algorithm was developed using HRNet, with 926 training images and 232 validation images extracted from 62 scenes of robot-assisted gastrectomy videos. During quantitative evaluation, the precision, recall, intersection over union (IoU), and Dice coefficients were calculated based on the surgeons' ground truth and the AI-inferred image from 80 test images. During the qualitative evaluation, 10 surgeons answered two questions related to sensitivity and similarity for assessing clinical usefulness. RESULTS: The precision, recall, IoU, and Dice coefficients were 0.70, 0.59, 0.46, and 0.61, respectively. Regarding sensitivity, the average score for pancreas recognition by AI was 4.18 out of 5 points (1 = lowest recognition [less than 50%]; 5 = highest recognition [more than 90%]). Regarding similarity, only 54% of the AI-inferred images were correctly differentiated from the ground truth. CONCLUSIONS: Our surgical AI system precisely highlighted the pancreas during robot-assisted gastrectomy at a level that was convincing to surgeons. This technology may prevent misrecognition of the pancreas by surgeons, thus leading to fewer POPFs.

An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves. METHODS: The model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos. RESULTS: The mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465-0.639) and 0.292 (range 0.0238-0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves. CONCLUSIONS: In laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition.

The relationship of game genres, in-game purchases, and playing duration with probable gaming disorder in two independent, large-scale online surveys of Japanese adults.

Background and aims: Game genres, availability on smartphones, in-game purchases, and playing duration, have been thought to influence Gaming Disorder (GD). However, little research has comprehensively examined their relationships with GD. Therefore, we examined the relationship between GD, in-game purchases, gaming duration via consoles and smartphones, and genres of smartphone games. Study 1 was based on self-reports, and Study 2 included objective data to clarify these associations. Methods: We conducted two independent online surveys that collected sociodemographic data, game use patterns, and psychopathological assessment data, including GD severity (Study 1: N = 32,690; Study 2: N = 3,163). General mental illness scores and objective gaming time were also collected in Study 2. Results: In Study 1, in-game purchases, several gaming genres, and subjective gaming duration were positively associated with probable GD. On the other hand, interactions between card games and loot box charges were negatively related to probable GD. In Study 2, objective gaming times of most game genres were not associated with GD. Although the correlation between subjective and objective gaming duration was moderate, their correlations with GD differed. Discussion and conclusion: These results suggest the complexity of relationships between GD and in-game purchases, genres, and gaming duration. Results of this study suggest the importance of proper assessment of GD reflecting actual functional impairment in social life. Future studies should improve and update evaluation of assessments for gaming.

Novel navigation for laparoscopic cholecystectomy fusing artificial intelligence and indocyanine green fluorescent imaging.

This preliminary study is the first to demonstrate that AI can precisely identify loose connective tissue during laparoscopic cholecystectomy and ICG fluorescent cholangiography. Tashiro and colleagues conclude that this novel real-time navigation modality fusing AI and ICG fluorescent imaging may enhance safety and provide more reliable laparoscopic or robotic surgery.

Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system.

The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI algorithm was developed to identify X and Y coordinates of surgical instrument tips accurately from video. Kinematic analysis including fluctuation analysis was performed on 18 laparoscopic distal gastrectomy videos from three expert and three novice surgeons (3 videos/surgeon, 11.6 h, 1,254,010 frames). Analysis showed the expert surgeon cohort moved more efficiently and regularly, with significantly less operation time and total travel distance. Instrument tip movement did not differ in velocity, acceleration, or jerk between skill levels. The evaluation index of fluctuation ß was significantly higher in experts. ROC curve cutoff value at 1.4 determined sensitivity and specificity of 77.8% for experts and novices. Despite the small sample, this study suggests AI-based object detection with fluctuation analysis is promising because skill evaluation can be calculated in real time with potential for peri-operational evaluation.

Evidence of tosufloxacin deposition in the kidneys of a patient presenting with crystal nephropathy.

Tosufloxacin tosilate is classified as a new quinolone antibacterial agent, which has been reported to cause crystal nephropathy. However, the origin of these crystal deposits has not yet been elucidated. We encountered a case of renal failure that progressed slowly owing to crystal-forming interstitial nephritis after long-term exposure to tosufloxacin. Mass spectrometry of the renal specimens revealed that tosufloxacin was deposited in the kidneys. The patient's renal function improved slowly with the withdrawal of tosufloxacin and steroid therapy. This is the first case to demonstrate the presence of crystal deposits consisting of tosufloxacin.

Carbon ion radiotherapy for mesonephric adenocarcinoma of the uterine cervix: a case report.

BACKGROUND: Mesonephric adenocarcinoma is an extremely rare subtype of uterine cervical cancer that is associated with a poor prognosis and for which a standardized treatment protocol has not been established. Carbon ion radiotherapy (CIRT) is an emerging radiotherapy modality that has been shown to have a favorable anti-tumor effect, even for tumors resistant to conventional photon radiotherapy or chemotherapy. However, there is no report on CIRT outcomes for mesonephric adenocarcinoma of the uterine cervix. CASE PRESENTATION: We treated a 47-year-old Japanese woman with mesonephric adenocarcinoma of the uterine cervix (T2bN0M0 and stage IIB according to the 7th edition of the Union for International Cancer Control and International Federation of Gynecology and Obstetrics, respectively) with CIRT combined with brachytherapy and concurrent chemotherapy. CIRT consisted of whole pelvic irradiation and boost irradiation to the gross tumor; 36.0 Gy (relative biological effectiveness [RBE]) in 12 fractions and 19.2 Gy (RBE) in 4 fractions, respectively, performed once a day, four times per week. Computed tomography-based image-guided adaptive brachytherapy was performed after completion of CIRT, for which the D90 (i.e., the dose prescribed to 90% of the target volume) for the high-risk clinical target volume was 20.4 Gy in a total of 3 sessions in 2 weeks. A weekly cisplatin (40 mg/m2) dose was administered concomitantly with the radiotherapy for a total of five courses. From 4 months post-CIRT, the patient developed metastasis of the lung, with a total of 10 lung metastases over 70 months; these lesions were treated on each occasion by photon stereotactic body radiotherapy and/or systemic therapy. At 8 years from initial treatment (i.e., 2 years after the last treatment), the patient is alive without any evidence of recurrence and maintains a high quality of life. CONCLUSIONS: This is the first report of CIRT for treatment of mesonephric adenocarcinoma of the uterine cervix. The present case indicates the potential efficacy of CIRT in combination with brachytherapy for treatment of this disease.

Accuracy of thoracic nerves recognition for surgical support system using artificial intelligence.

We developed a surgical support system that visualises important microanatomies using artificial intelligence (AI). This study evaluated its accuracy in recognising the thoracic nerves during lung cancer surgery. Recognition models were created with deep learning using images precisely annotated for nerves. Computational evaluation was performed using the Dice index and the Jaccard index. Four general thoracic surgeons evaluated the accuracy of nerve recognition. Further, the differences in time lag, image quality and smoothness of movement between the AI system and surgical monitor were assessed. Ratings were made using a five-point scale. The computational evaluation was relatively favourable, with a Dice index of 0.56 and a Jaccard index of 0.39. The AI system was used for 10 thoracoscopic surgeries for lung cancer. The accuracy of thoracic nerve recognition was satisfactory, with a recall score of 4.5 ± 0.4 and a precision score of 4.0 ± 0.9. Though smoothness of motion (3.2 ± 0.4) differed slightly, nearly no difference in time lag (4.9 ± 0.3) and image quality (4.6 ± 0.5) between the AI system and the surgical monitor were observed. In conclusion, the AI surgical support system has a satisfactory accuracy in recognising the thoracic nerves.