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.

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.

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.

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.

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.

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.

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.

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.

Color-coded laparoscopic liver resection using artificial intelligence: A preliminary study.

Tashiro and colleagues demonstrated for the first time that an artificial intelligence system can precisely identify intrahepatic vascular structures during laparoscopic liver resection in real time through color coding under bleeding and indocyanine green fluorescent imaging. The system supports real-time navigation and offers potentially safer laparoscopic or robotic liver surgery.

Development of a Novel Artificial Intelligence System for Laparoscopic Hepatectomy.

BACKGROUND/AIM: Laparoscopic hepatectomy (LH) requires accurate visualization and appropriate handling of hepatic veins and the Glissonean pedicle that suddenly appear during liver dissection. Failure to recognize these structures can cause injury, resulting in severe bleeding and bile leakage. This study aimed to develop a novel artificial intelligence (AI) system that assists in the visual recognition and color presentation of tubular structures to correct the recognition gap among surgeons. PATIENTS AND METHODS: Annotations were performed on over 350 video frames capturing LH, after which a deep learning model was developed. The performance of the AI was evaluated quantitatively using intersection over union (IoU) and Dice coefficients, as well as qualitatively using a two-item questionnaire on sensitivity and misrecognition completed by 10 hepatobiliary surgeons. The usefulness of AI in medical education was qualitatively evaluated by 10 medical students and residents. RESULTS: The AI model was able to individually recognize and colorize hepatic veins and the Glissonean pedicle in real time. The IoU and Dice coefficients were 0.42 and 0.53, respectively. Surgeons provided a mean sensitivity score of 4.24±0.89 (from 1 to 5; Excellent) and a mean misrecognition score of 0.12±0.33 (from 0 to 4; Fail). Medical students and residents assessed the AI to be very useful (mean usefulness score, 1.86±0.35; from 0 to 2; Excellent). CONCLUSION: The novel AI presented was able to assist surgeons in the intraoperative recognition of microstructures and address the recognition gap among surgeons to ensure a safer and more accurate LH.

Resting phase-administration of lemborexant ameliorates sleep and glucose tolerance in type 2 diabetic mice.

Sleep disorders are associated with increased risk of obesity and type 2 diabetes. Lemborexant, a dual orexin receptor antagonist (DORA), is clinically used to treat insomnia. However, the influence of lemborexant on sleep and glucose metabolism in type 2 diabetic state has remained unknown. In the present study, we investigated the effect of lemborexant in type 2 diabetic db/db mice exhibiting both sleep disruption and glucose intolerance. Single administration of lemborexant at the beginning of the light phase (i.e., resting phase) acutely increased total time spent in non-rapid eye movement (NREM) and REM sleep in db/db mice. Durations of NREM sleep-, REM sleep-, and wake-episodes were also increased by this administration. Daily resting-phase administration of lemborexant for 3-6 weeks improved glucose tolerance without changing body weight and glucose-stimulated insulin secretion in db/db mice. Similar improvement of glucose tolerance was caused by daily resting-phase administration of lemborexant in obese C57BL/6J mice fed high fat diet, whereas no such effect was observed in non-diabetic db/m+ mice. Diabetic db/db mice treated daily with lemborexant exhibited increased locomotor activity in the dark phase (i.e., awake phase), although they did not show any behavioral abnormality in the Y-maze, elevated plus maze, and forced swim tests. These results suggest that timely promotion of sleep by lemborexant improved the quality of wakefulness in association with increased physical activity during the awake phase, and these changes may underlie the amelioration of glucose metabolism under type 2 diabetic conditions.

[A Case of Advanced Esophagogastric Junction Adenocarcinoma with Enlarged Lymph Node Metastases of the Middle Mediastinum and Intraperitoneal That Exhibited pCR by Preoperative SOX Therapy].

A 79-year-old man was diagnosed with esophagogastric junction adenocarcinoma, cT3N3M0, cStage Ⅲ, including enlarged lymph node metastases(Bulky N)in the middle mediastinum and intraperitoneal. A total of 2 cycles of S-1 plus oxaliplatin(SOX)was administered. After neoadjuvant chemotherapy, the primary tumor and enlarged lymph nodes had greatly decreased in size. Subsequently, thoracoscopic subtotal esophagectomy and reconstruction with a gastric tube were performed. Histopathological examinations showed no residual cancer cells in the primary lesion and dissected lymph nodes (pathological complete response). Preoperative chemotherapy containing SOX could be a useful treatment strategy for patients with esophagogastric junction adenocarcinoma with enlarged lymph node metastasis.

Validity, reliability, and correlates of the Smartphone Addiction Scale-Short Version among Japanese adults.

BACKGROUND: The short version of the smartphone addiction scale (SAS-SV) is widely used to measure problematic smartphone use (PSU). This study examined the validity and reliability of the SAS-SV among Japanese adults, as well as cross-sectional and longitudinal associations with relevant mental health traits and problems. METHODS: Datasets from a larger project on smartphone use and mental health were used to conduct two studies. Participants were adults aged over 20 years who carried a smartphone. RESULTS: Study 1 (n = 99,156) showed the acceptable internal consistency and structural validity of the SAS-SV with a bifactor model with three factors. For the test-retest reliability of the SAS-SV, the intraclass correlation coefficient (ICC) was .70, 95% CI [.69, 70], when the SAS-SV was measured seven and twelve months apart (n = 20,389). Study 2 (n = 3419) revealed that when measured concurrently, the SAS-SV was strongly positively correlated with another measure of PSU and moderately correlated with smartphone use time, problematic internet use (PIU), depression, the attentional factor of impulsiveness, and symptoms related to attention-deficit hyperactivity disorder and obsessive-compulsive disorder. When measured 12 months apart, the SAS-SV was positively strongly associated with another measure of PSU and PIU and moderately associated with depression. DISCUSSION: The structural validity of the SAS-SV appeared acceptable among Japanese adults with the bifactor model. The reliability of the SAS-SV was demonstrated in the subsequent seven- and twelve-month associations. CONCLUSION: The cross-sectional and longitudinal associations of the SAS-SV provided further evidence regarding PSU characteristics.

Reducing the risk of postoperative pancreatic fistula in radical gastrectomy: pre-assessment with computed tomography for the diagnosis of pancreatic steatosis.

PURPOSE: To determine whether pancreatic steatosis (PS) is associated with the risk of postoperative pancreatic fistula (POPF) after radical gastrectomy, and if so, to investigate whether pre-assessment by diagnostic imaging can mitigate the risk. METHODS: The clinical records of 276 patients with cStage I gastric cancer who underwent laparoscopic gastrectomy with D1 + lymphadenectomy between 2012 and 2015 were reviewed. In the first phase up to July 2013 (n = 138), PS was classified from computed tomography (CT) findings into type S (superficial fat deposition) or type D (diffuse fatty replacement) and examined for association with POPF. In the second phase (n = 138), the preoperative CT assessment of PS was routinized. Separate samples from pancreatoduodenectomy consistent with each type were histologically examined. RESULTS: In the first phase, the incidence of POPF was significantly higher in group S, but not in group D, compared with normal pancreas (16.3% and 9.1% vs. 3.6%, respectively; P = 0.03). The drain amylase level was lowest in group D, reflecting exocrine insufficiency. Histologically, the loose connective-tissue space between the fat infiltrating the pancreas and the peripancreatic fat containing the lymph nodes was unclear in type D but conserved in type S. In the second phase, surgery was performed with more intention on accurately tracing the dissection plane and significantly lowered incidence of POPF in Group S (16.3% to 2.1%; P = 0.047). CONCLUSION: Peripancreatic lymphadenectomy is more challenging and likely to cause POPF in patients with PS. However, the risk may be reduced using appropriate dissection techniques based on the CT pre-assessment findings.

Multiple time measurements of multidimensional psychiatric states from immediately before the COVID-19 pandemic to one year later: a longitudinal online survey of the Japanese population.

The coronavirus disease 2019 (COVID-19) pandemic has profoundly affected the mental health of both infected and uninfected people. Although most psychiatric disorders have highly overlapping genetic and pathogenic backgrounds, most studies investigating the impact of the pandemic have examined only single psychiatric disorders. It is necessary to examine longitudinal trajectories of factors that modulate psychiatric states across multiple dimensions. About 2274 Japanese citizens participated in online surveys presented in December 2019 (before the pandemic), August 2020, Dec 2020, and April 2021. These surveys included nine questionnaires on psychiatric symptoms, such as depression and anxiety. Multidimensional psychiatric time-series data were then decomposed into four principal components. We used generalized linear models to identify modulating factors for the effects of the pandemic on these components. The four principal components can be interpreted as a general psychiatric burden, social withdrawal, alcohol-related problems, and depression/anxiety. Principal components associated with general psychiatric burden and depression/anxiety peaked during the initial phase of the pandemic. They were further exacerbated by the economic burden the pandemic imposed. In contrast, principal components associated with social withdrawal showed a delayed peak, with human relationships as an important risk modulating factor. In addition, being female was a risk factor shared across all components. Our results show that COVID-19 has imposed a large and varied burden on the Japanese population since the commencement of the pandemic. Although components related to the general psychiatric burden remained elevated, peak intensities differed between components related to depression/anxiety and those related to social withdrawal. These results underline the importance of using flexible monitoring and mitigation strategies for mental problems, according to the phase of the pandemic.

Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy.

The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons' experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335-0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45-3.95). The mean misrecognition score was a low 0.14 (range 0-0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.

Colorectal Cancer Screening Outcomes of 2412 Prostate Cancer Patients Considered for Carbon Ion Radiotherapy.

Colorectal cancer (CRC) screening is effective for detecting cancer in average-risk adults. For prostate cancer (PCa) patients considered for carbon ion radiotherapy (CIRT), pre-treatment CRC screening is performed empirically to avoid post-treatment colonoscopic manipulation. However, the outcomes of screening this population remain unclear. Here, we compared the outcomes of routine pre-CIRT CRC screening of 2412 PCa patients at average risk for CRC with data from two published datasets: the Japan National Cancer Registry (JNCR) and a series of 17 large-scale screening studies analyzing average-risk adults. The estimated prevalence rate was calculated using the pooled sensitivity elucidated by a previous meta-analysis. Consequently, 28 patients (1.16%) were diagnosed with CRC. CRC morbidity was significantly associated with high pre-treatment levels of prostate-specific antigen (p = 0.023). The screening positivity rate in this study cohort exceeded the annual incidence reported in the JNCR for most age brackets. Furthermore, the estimated prevalence rate in this study cohort (1.46%) exceeded that reported in all 17 large-scale studies, making the result an outlier (p = 0.005). These data indicate the possibility that the prevalence of CRC in PCa patients is greater than that in general average-risk adults, warranting further research in a prospective setting.

Prevalence and risk factors of internet gaming disorder and problematic internet use before and during the COVID-19 pandemic: A large online survey of Japanese adults.

Internet gaming disorder (IGD) and problematic internet use (PIU) are becoming increasingly detrimental to modern society, with serious consequences for daily functioning. IGD and PIU may be exacerbated by lifestyle changes imposed by the coronavirus 2019 (COVID-19) pandemic. This study investigated changes in IGD and PIU during the pandemic and risk factors for them. This study is a part of a larger online study of problematic smartphone use in Japan, originally planned in 2019, and expanded in August 2020 to include the impact of COVID-19. 51,246 adults completed an online survey during the pandemic (August 2020), in Japan. Of these, 3,938 had also completed the survey before the onset of the pandemic (December 2019) and were used as the study population to determine how the pandemic has influenced IGD and PIU. IGD was assessed using the Internet Gaming Disorder Scale (IGDS). PIU was measured using the Compulsive Internet Use Scale (CIUS). The prevalence of probable IGD during COVID-19 was 4.1% overall [95%CI, 3.9%-4.2%] (N = 51,246), and 8.6% among younger people (age < 30), 1-2.5% higher than reported before the pandemic. Probable PIU was 7.8% overall [95%CI, 7.6%-8.1%], and 17.0% [95%CI, 15.9%-18.2%] among younger people, 3.2-3.7% higher than reported before the pandemic. Comparisons before and during the pandemic, revealed that probable IGD prevalence has increased 1.6 times, and probable PIU prevalence by 1.5 times (IGD: χ2= 619.9, p < .001, PIU: χ2= 594.2, p < .001). Youth (age < 30) and COVID-19 infection were strongly associated with IGD exacerbation (odds ratio, 2.10 [95%CI, 1.18 to 3.75] and 5.67 [95%CI, 1.33 to 24.16]). Internet gaming disorder and problematic internet use appear to be aggravated by the pandemic. In particular, younger persons and people infected with COVID-19 are at higher risk for Internet Gaming Disorder. Prevention and treatment of these problems are needed.