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  1. Article ; Online: Validation of a novel virtual reality simulation system with the focus on training for surgical dissection during laparoscopic sigmoid colectomy.

    Mori, Takashi / Ikeda, Koji / Takeshita, Nobuyoshi / Teramura, Koichi / Ito, Masaaki

    BMC surgery

    2022  Volume 22, Issue 1, Page(s) 12

    Abstract: Background: Mastery of technical skills is one of the fundamental goals of surgical training for novices. Meanwhile, performing laparoscopic procedures requires exceptional surgical skills compared to open surgery. However, it is often difficult for ... ...

    Abstract Background: Mastery of technical skills is one of the fundamental goals of surgical training for novices. Meanwhile, performing laparoscopic procedures requires exceptional surgical skills compared to open surgery. However, it is often difficult for trainees to learn through observation and practice only. Virtual reality (VR)-based surgical simulation is expanding and rapidly advancing. A major obstacle for laparoscopic trainees is the difficulty of well-performed dissection. Therefore, we developed a new VR simulation system, Lap-PASS LP-100, which focuses on training to create proper tension on the tissue in laparoscopic sigmoid colectomy dissection. This study aimed to validate this new VR simulation system.
    Methods: A total of 50 participants were asked to perform medial dissection of the meso-sigmoid colon on the VR simulator. Forty-four surgeons and six non-medical professionals working in the National Cancer Center Hospital East, Japan, were enrolled in this study. The surgeons were: laparoscopic surgery experts with > 100 laparoscopic surgeries (LS), 21 were novices with experience < 100 LS, and five without previous experience in LS. The participants' surgical performance was evaluated by three blinded raters using Global Operative Assessment of Laparoscopic Skills (GOALS).
    Results: There were significant differences (P-values < 0.044) in all GOALS items between the non-medical professionals and surgeons. The experts were significantly superior to the novices in one item of GOALS: efficiency ([4(4-5) vs. 4(3-4)], with a 95% confidence interval, p = 0.042). However, both bimanual dexterity and total score in the experts were not statistically different but tended to be higher than in the novices.
    Conclusions: Our study demonstrated a full validation of our new system. This could detect the surgeons' ability to perform surgical dissection and suggest that this VR simulator could be an effective training tool. This surgical VR simulator might have tremendous potential to enhance training for surgeons.
    MeSH term(s) Clinical Competence ; Colectomy ; Colon, Sigmoid ; Computer Simulation ; Dissection ; Humans ; Laparoscopy ; Simulation Training ; User-Computer Interface ; Virtual Reality
    Language English
    Publishing date 2022-01-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2050442-1
    ISSN 1471-2482 ; 1471-2482
    ISSN (online) 1471-2482
    ISSN 1471-2482
    DOI 10.1186/s12893-021-01441-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Novel external reinforcement device for gastrointestinal anastomosis in an experimental study.

    Hasegawa, Hiro / Takeshita, Nobuyoshi / Hyon, Woogi / Hyon, Suong-Hyu / Ito, Masaaki

    BMC surgery

    2023  Volume 23, Issue 1, Page(s) 121

    Abstract: Background: Anastomotic leakage has been reported to occur when the load on the anastomotic site exceeds the resistance created by sutures, staples, and early scars. It may be possible to avoid anastomotic leakage by covering and reinforcing the ... ...

    Abstract Background: Anastomotic leakage has been reported to occur when the load on the anastomotic site exceeds the resistance created by sutures, staples, and early scars. It may be possible to avoid anastomotic leakage by covering and reinforcing the anastomotic site with a biocompatible material. The aim of this study was to evaluate the safety and feasibility of a novel external reinforcement device for gastrointestinal anastomosis in an experimental model.
    Methods: A single pig was used in this non-survival study, and end-to-end anastomoses were created in six small bowel loops by a single-stapling technique using a circular stapler. Three of the six anastomoses were covered with a novel external reinforcement device. Air was injected, a pressure test of each anastomosis was performed, and the bursting pressure was measured.
    Results: Reinforcement of the anastomotic site with the device was successfully performed in all anastomoses. The bursting pressure was 76.1 ± 5.7 mmHg in the control group, and 126.8 ± 6.8 mmHg in the device group, respectively. The bursting pressure in the device group was significantly higher than that in the control group (p = 0.0006).
    Conclusions: The novel external reinforcement device was safe and feasible for reinforcing the anastomoses in the experimental model.
    MeSH term(s) Swine ; Animals ; Anastomotic Leak/prevention & control ; Anastomotic Leak/surgery ; Anastomosis, Surgical/methods ; Intestine, Small/surgery ; Surgical Stapling/methods ; Cicatrix
    Language English
    Publishing date 2023-05-11
    Publishing country England
    Document type Journal Article
    ZDB-ID 2050442-1
    ISSN 1471-2482 ; 1471-2482
    ISSN (online) 1471-2482
    ISSN 1471-2482
    DOI 10.1186/s12893-023-02027-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Automatic purse-string suture skill assessment in transanal total mesorectal excision using deep learning-based video analysis.

    Kitaguchi, Daichi / Teramura, Koichi / Matsuzaki, Hiroki / Hasegawa, Hiro / Takeshita, Nobuyoshi / Ito, Masaaki

    BJS open

    2023  Volume 7, Issue 2

    Abstract: Background: Purse-string suture in transanal total mesorectal excision is a key procedural step. The aims of this study were to develop an automatic skill assessment system for purse-string suture in transanal total mesorectal excision using deep ... ...

    Abstract Background: Purse-string suture in transanal total mesorectal excision is a key procedural step. The aims of this study were to develop an automatic skill assessment system for purse-string suture in transanal total mesorectal excision using deep learning and to evaluate the reliability of the score output from the proposed system.
    Methods: Purse-string suturing extracted from consecutive transanal total mesorectal excision videos was manually scored using a performance rubric scale and computed into a deep learning model as training data. Deep learning-based image regression analysis was performed, and the purse-string suture skill scores predicted by the trained deep learning model (artificial intelligence score) were output as continuous variables. The outcomes of interest were the correlation, assessed using Spearman's rank correlation coefficient, between the artificial intelligence score and the manual score, purse-string suture time, and surgeon's experience.
    Results: Forty-five videos obtained from five surgeons were evaluated. The mean(s.d.) total manual score was 9.2(2.7) points, the mean(s.d.) total artificial intelligence score was 10.2(3.9) points, and the mean(s.d.) absolute error between the artificial intelligence and manual scores was 0.42(0.39). Further, the artificial intelligence score significantly correlated with the purse-string suture time (correlation coefficient = -0.728) and surgeon's experience (P< 0.001).
    Conclusion: An automatic purse-string suture skill assessment system using deep learning-based video analysis was shown to be feasible, and the results indicated that the artificial intelligence score was reliable. This application could be expanded to other endoscopic surgeries and procedures.
    MeSH term(s) Humans ; Artificial Intelligence ; Deep Learning ; Reproducibility of Results ; Sutures ; Rectal Neoplasms
    Language English
    Publishing date 2023-03-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2474-9842
    ISSN (online) 2474-9842
    DOI 10.1093/bjsopen/zrac176
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives.

    Kitaguchi, Daichi / Takeshita, Nobuyoshi / Hasegawa, Hiro / Ito, Masaaki

    Annals of gastroenterological surgery

    2021  Volume 6, Issue 1, Page(s) 29–36

    Abstract: Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a ... ...

    Abstract Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery-related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real-world information acquisition by computers, the terminology "CV" is extensive, and ranges from hardware for image sensing to AI-based image recognition. AI-based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision-making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI-related research and development in the field of surgery.
    Language English
    Publishing date 2021-10-08
    Publishing country Japan
    Document type Journal Article ; Review
    ISSN 2475-0328
    ISSN (online) 2475-0328
    DOI 10.1002/ags3.12513
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  5. Article ; Online: Novel oxygen saturation imaging endoscopy to assess anastomotic integrity in a porcine ischemia model.

    Hasegawa, Hiro / Takeshita, Nobuyoshi / Ito, Masaaki

    BMC surgery

    2020  Volume 20, Issue 1, Page(s) 250

    Abstract: Background: Establishing anastomotic integrity is crucial for avoiding anastomotic complications in colorectal surgery. This study aimed to evaluate the safety and feasibility of assessing anastomotic integrity using novel oxygen saturation imaging ... ...

    Abstract Background: Establishing anastomotic integrity is crucial for avoiding anastomotic complications in colorectal surgery. This study aimed to evaluate the safety and feasibility of assessing anastomotic integrity using novel oxygen saturation imaging endoscopy in a porcine ischemia model.
    Methods: In three pigs, a new endoscope system was used to check the mechanical completeness of the anastomosis and capture the tissue oxygen saturation (StO
    Results: The completeness of the anastomoses was confirmed by the absence of air leakage. Intraluminal oxygen saturation imaging was successfully performed in all animals. There was no significant difference in the StO
    Conclusion: Novel oxygen saturation imaging endoscopy was safe and feasible to assess the anastomotic integrity in the experimental model.
    MeSH term(s) Anastomosis, Surgical/adverse effects ; Anastomotic Leak/diagnostic imaging ; Anastomotic Leak/etiology ; Animals ; Disease Models, Animal ; Endoscopy/methods ; Feasibility Studies ; Female ; Hemoglobins/analysis ; Ischemia/blood ; Ischemia/diagnostic imaging ; Oxygen/analysis ; Oxyhemoglobins/analysis ; Rectum/blood supply ; Rectum/diagnostic imaging ; Rectum/metabolism ; Rectum/surgery ; Swine ; Treatment Outcome
    Chemical Substances Hemoglobins ; Oxyhemoglobins ; deoxyhemoglobin (9008-02-0) ; Oxygen (S88TT14065)
    Language English
    Publishing date 2020-10-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2050442-1
    ISSN 1471-2482 ; 1471-2482
    ISSN (online) 1471-2482
    ISSN 1471-2482
    DOI 10.1186/s12893-020-00913-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments.

    Kitaguchi, Daichi / Fujino, Toru / Takeshita, Nobuyoshi / Hasegawa, Hiro / Mori, Kensaku / Ito, Masaaki

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 12575

    Abstract: Clarifying the generalizability of deep-learning-based surgical-instrument segmentation networks in diverse surgical environments is important in recognizing the challenges of overfitting in surgical-device development. This study comprehensively ... ...

    Abstract Clarifying the generalizability of deep-learning-based surgical-instrument segmentation networks in diverse surgical environments is important in recognizing the challenges of overfitting in surgical-device development. This study comprehensively evaluated deep neural network generalizability for surgical instrument segmentation using 5238 images randomly extracted from 128 intraoperative videos. The video dataset contained 112 laparoscopic colorectal resection, 5 laparoscopic distal gastrectomy, 5 laparoscopic cholecystectomy, and 6 laparoscopic partial hepatectomy cases. Deep-learning-based surgical-instrument segmentation was performed for test sets with (1) the same conditions as the training set; (2) the same recognition target surgical instrument and surgery type but different laparoscopic recording systems; (3) the same laparoscopic recording system and surgery type but slightly different recognition target laparoscopic surgical forceps; (4) the same laparoscopic recording system and recognition target surgical instrument but different surgery types. The mean average precision and mean intersection over union for test sets 1, 2, 3, and 4 were 0.941 and 0.887, 0.866 and 0.671, 0.772 and 0.676, and 0.588 and 0.395, respectively. Therefore, the recognition accuracy decreased even under slightly different conditions. The results of this study reveal the limited generalizability of deep neural networks in the field of surgical artificial intelligence and caution against deep-learning-based biased datasets and models.Trial Registration Number: 2020-315, date of registration: October 5, 2020.
    MeSH term(s) Artificial Intelligence ; Laparoscopy/methods ; Neural Networks, Computer ; Surgical Instruments
    Language English
    Publishing date 2022-07-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-16923-8
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  7. Article ; Online: Automatic Surgical Skill Assessment System Based on Concordance of Standardized Surgical Field Development Using Artificial Intelligence.

    Igaki, Takahiro / Kitaguchi, Daichi / Matsuzaki, Hiroki / Nakajima, Kei / Kojima, Shigehiro / Hasegawa, Hiro / Takeshita, Nobuyoshi / Kinugasa, Yusuke / Ito, Masaaki

    JAMA surgery

    2023  Volume 158, Issue 8, Page(s) e231131

    Abstract: Importance: Automatic surgical skill assessment with artificial intelligence (AI) is more objective than manual video review-based skill assessment and can reduce human burden. Standardization of surgical field development is an important aspect of this ...

    Abstract Importance: Automatic surgical skill assessment with artificial intelligence (AI) is more objective than manual video review-based skill assessment and can reduce human burden. Standardization of surgical field development is an important aspect of this skill assessment.
    Objective: To develop a deep learning model that can recognize the standardized surgical fields in laparoscopic sigmoid colon resection and to evaluate the feasibility of automatic surgical skill assessment based on the concordance of the standardized surgical field development using the proposed deep learning model.
    Design, setting, and participants: This retrospective diagnostic study used intraoperative videos of laparoscopic colorectal surgery submitted to the Japan Society for Endoscopic Surgery between August 2016 and November 2017. Data were analyzed from April 2020 to September 2022.
    Interventions: Videos of surgery performed by expert surgeons with Endoscopic Surgical Skill Qualification System (ESSQS) scores higher than 75 were used to construct a deep learning model able to recognize a standardized surgical field and output its similarity to standardized surgical field development as an AI confidence score (AICS). Other videos were extracted as the validation set.
    Main outcomes and measures: Videos with scores less than or greater than 2 SDs from the mean were defined as the low- and high-score groups, respectively. The correlation between AICS and ESSQS score and the screening performance using AICS for low- and high-score groups were analyzed.
    Results: The sample included 650 intraoperative videos, 60 of which were used for model construction and 60 for validation. The Spearman rank correlation coefficient between the AICS and ESSQS score was 0.81. The receiver operating characteristic (ROC) curves for the screening of the low- and high-score groups were plotted, and the areas under the ROC curve for the low- and high-score group screening were 0.93 and 0.94, respectively.
    Conclusions and relevance: The AICS from the developed model strongly correlated with the ESSQS score, demonstrating the model's feasibility for use as a method of automatic surgical skill assessment. The findings also suggest the feasibility of the proposed model for creating an automated screening system for surgical skills and its potential application to other types of endoscopic procedures.
    MeSH term(s) Humans ; Artificial Intelligence ; Retrospective Studies ; Laparoscopy/methods ; Digestive System Surgical Procedures ; ROC Curve
    Language English
    Publishing date 2023-08-09
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2701841-6
    ISSN 2168-6262 ; 2168-6254
    ISSN (online) 2168-6262
    ISSN 2168-6254
    DOI 10.1001/jamasurg.2023.1131
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  8. Article ; Online: Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery.

    Kitaguchi, Daichi / Harai, Yuriko / Kosugi, Norihito / Hayashi, Kazuyuki / Kojima, Shigehiro / Ishikawa, Yuto / Yamada, Atsushi / Hasegawa, Hiro / Takeshita, Nobuyoshi / Ito, Masaaki

    The British journal of surgery

    2023  Volume 110, Issue 10, Page(s) 1355–1358

    MeSH term(s) Humans ; Artificial Intelligence ; Colorectal Surgery ; Laparoscopy ; Digestive System Surgical Procedures
    Language English
    Publishing date 2023-08-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2985-3
    ISSN 1365-2168 ; 0263-1202 ; 0007-1323 ; 1355-7688
    ISSN (online) 1365-2168
    ISSN 0263-1202 ; 0007-1323 ; 1355-7688
    DOI 10.1093/bjs/znad249
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Automatic surgical phase recognition-based skill assessment in laparoscopic distal gastrectomy using multicenter videos.

    Komatsu, Masaru / Kitaguchi, Daichi / Yura, Masahiro / Takeshita, Nobuyoshi / Yoshida, Mitsumasa / Yamaguchi, Masayuki / Kondo, Hibiki / Kinoshita, Takahiro / Ito, Masaaki

    Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association

    2023  Volume 27, Issue 1, Page(s) 187–196

    Abstract: Background: Gastric surgery involves numerous surgical phases; however, its steps can be clearly defined. Deep learning-based surgical phase recognition can promote stylization of gastric surgery with applications in automatic surgical skill assessment. ...

    Abstract Background: Gastric surgery involves numerous surgical phases; however, its steps can be clearly defined. Deep learning-based surgical phase recognition can promote stylization of gastric surgery with applications in automatic surgical skill assessment. This study aimed to develop a deep learning-based surgical phase-recognition model using multicenter videos of laparoscopic distal gastrectomy, and examine the feasibility of automatic surgical skill assessment using the developed model.
    Methods: Surgical videos from 20 hospitals were used. Laparoscopic distal gastrectomy was defined and annotated into nine phases and a deep learning-based image classification model was developed for phase recognition. We examined whether the developed model's output, including the number of frames in each phase and the adequacy of the surgical field development during the phase of supra-pancreatic lymphadenectomy, correlated with the manually assigned skill assessment score.
    Results: The overall accuracy of phase recognition was 88.8%. Regarding surgical skill assessment based on the number of frames during the phases of lymphadenectomy of the left greater curvature and reconstruction, the number of frames in the high-score group were significantly less than those in the low-score group (829 vs. 1,152, P < 0.01; 1,208 vs. 1,586, P = 0.01, respectively). The output score of the adequacy of the surgical field development, which is the developed model's output, was significantly higher in the high-score group than that in the low-score group (0.975 vs. 0.970, P = 0.04).
    Conclusion: The developed model had high accuracy in phase-recognition tasks and has the potential for application in automatic surgical skill assessment systems.
    MeSH term(s) Humans ; Stomach Neoplasms/surgery ; Laparoscopy/methods ; Gastroenterostomy ; Gastrectomy/methods
    Language English
    Publishing date 2023-12-01
    Publishing country Japan
    Document type Multicenter Study ; Journal Article
    ZDB-ID 1463526-4
    ISSN 1436-3305 ; 1436-3291
    ISSN (online) 1436-3305
    ISSN 1436-3291
    DOI 10.1007/s10120-023-01450-w
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  10. Article ; Online: Intraoperative artificial intelligence system identifying liver vessels in laparoscopic liver resection: a retrospective experimental study.

    Une, Norikazu / Kobayashi, Shin / Kitaguchi, Daichi / Sunakawa, Taiki / Sasaki, Kimimasa / Ogane, Tateo / Hayashi, Kazuyuki / Kosugi, Norihito / Kudo, Masashi / Sugimoto, Motokazu / Hasegawa, Hiro / Takeshita, Nobuyoshi / Gotohda, Naoto / Ito, Masaaki

    Surgical endoscopy

    2024  Volume 38, Issue 2, Page(s) 1088–1095

    Abstract: Background: The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to ... ...

    Abstract Background: The precise recognition of liver vessels during liver parenchymal dissection is the crucial technique for laparoscopic liver resection (LLR). This retrospective feasibility study aimed to develop artificial intelligence (AI) models to recognize liver vessels in LLR, and to evaluate their accuracy and real-time performance.
    Methods: Images from LLR videos were extracted, and the hepatic veins and Glissonean pedicles were labeled separately. Two AI models were developed to recognize liver vessels: the "2-class model" which recognized both hepatic veins and Glissonean pedicles as equivalent vessels and distinguished them from the background class, and the "3-class model" which recognized them all separately. The Feature Pyramid Network was used as a neural network architecture for both models in their semantic segmentation tasks. The models were evaluated using fivefold cross-validation tests, and the Dice coefficient (DC) was used as an evaluation metric. Ten gastroenterological surgeons also evaluated the models qualitatively through rubric.
    Results: In total, 2421 frames from 48 video clips were extracted. The mean DC value of the 2-class model was 0.789, with a processing speed of 0.094 s. The mean DC values for the hepatic vein and the Glissonean pedicle in the 3-class model were 0.631 and 0.482, respectively. The average processing time for the 3-class model was 0.097 s. Qualitative evaluation by surgeons revealed that false-negative and false-positive ratings in the 2-class model averaged 4.40 and 3.46, respectively, on a five-point scale, while the false-negative, false-positive, and vessel differentiation ratings in the 3-class model averaged 4.36, 3.44, and 3.28, respectively, on a five-point scale.
    Conclusion: We successfully developed deep-learning models that recognize liver vessels in LLR with high accuracy and sufficient processing speed. These findings suggest the potential of a new real-time automated navigation system for LLR.
    MeSH term(s) Humans ; Artificial Intelligence ; Retrospective Studies ; Liver/diagnostic imaging ; Liver/surgery ; Liver/blood supply ; Hepatectomy/methods ; Laparoscopy/methods
    Language English
    Publishing date 2024-01-12
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 639039-0
    ISSN 1432-2218 ; 0930-2794
    ISSN (online) 1432-2218
    ISSN 0930-2794
    DOI 10.1007/s00464-023-10637-2
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