Automatic Surgical Skill Assessment System Based on Concordance of Standardized Surgical Field Development Using Artificial Intelligence.

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.

Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study.

BACKGROUND: The preservation of autonomic nerves is the most important factor in maintaining genitourinary function in colorectal surgery; however, these nerves are not clearly recognisable, and their identification is strongly affected by the surgical ability. Therefore, this study aimed to develop a deep learning model for the semantic segmentation of autonomic nerves during laparoscopic colorectal surgery and to experimentally verify the model through intraoperative use and pathological examination. MATERIALS AND METHODS: The annotation data set comprised videos of laparoscopic colorectal surgery. The images of the hypogastric nerve (HGN) and superior hypogastric plexus (SHP) were manually annotated under a surgeon's supervision. The Dice coefficient was used to quantify the model performance after five-fold cross-validation. The model was used in actual surgeries to compare the recognition timing of the model with that of surgeons, and pathological examination was performed to confirm whether the samples labelled by the model from the colorectal branches of the HGN and SHP were nerves. RESULTS: The data set comprised 12 978 video frames of the HGN from 245 videos and 5198 frames of the SHP from 44 videos. The mean (±SD) Dice coefficients of the HGN and SHP were 0.56 (±0.03) and 0.49 (±0.07), respectively. The proposed model was used in 12 surgeries, and it recognised the right HGN earlier than the surgeons did in 50.0% of the cases, the left HGN earlier in 41.7% of the cases and the SHP earlier in 50.0% of the cases. Pathological examination confirmed that all 11 samples were nerve tissue. CONCLUSION: An approach for the deep-learning-based semantic segmentation of autonomic nerves was developed and experimentally validated. This model may facilitate intraoperative recognition during laparoscopic colorectal surgery.

Universal meta-competencies of operative performances: a literature review and qualitative synthesis.

BACKGROUND: Prioritizing patient health is essential, and given the risk of mortality, surgical techniques should be objectively evaluated. However, there is no comprehensive cross-disciplinary system that evaluates skills across all aspects among surgeons of varying levels. Therefore, this study aimed to uncover universal surgical competencies by decomposing and reconstructing specific descriptions in operative performance assessment tools, as the basis of building automated evaluation system using computer vision and machine learning-based analysis. METHODS: The study participants were primarily expert surgeons in the gastrointestinal surgery field and the methodology comprised data collection, thematic analysis, and validation. For the data collection, participants identified global operative performance assessment tools according to detailed inclusion and exclusion criteria. Thereafter, thematic analysis was used to conduct detailed analyses of the descriptions in the tools where specific rules were coded, integrated, and discussed to obtain high-level concepts, namely, "Skill meta-competencies." "Skill meta-competencies" was recategorized for data validation and reliability assurance. Nine assessment tools were selected based on participant criteria. RESULTS: In total, 189 types of skill performances were extracted from the nine tool descriptions and organized into the following five competencies: (1) Tissue handling, (2) Psychomotor skill, (3) Efficiency, (4) Dissection quality, and (5) Exposure quality. The evolutionary importance of these competences' different evaluation targets and purpose over time were assessed; the results showed relatively high reliability, indicating that the categorization was reproducible. The inclusion of basic (tissue handling, psychomotor skill, and efficiency) and advanced (dissection quality and exposure quality) skills in these competencies enhanced the tools' comprehensiveness. CONCLUSIONS: The competencies identified to help surgeons formalize and implement tacit knowledge of operative performance are highly reproducible. These results can be used to form the basis of an automated skill evaluation system and help surgeons improve the provision of care and training, consequently, improving patient prognosis.

[Shared Decision Making with a Breast Cancer Case Diagnosed During Pregnancy-A Case Report].

Shared decision making(SDM)plays a crucial role in treatment discussions for pregnant patients with breast cancer. A woman in her 30s was diagnosed with StageⅠbreast cancer during the 20th week of her pregnancy. In SDM sessions, we proposed a total mastectomy and axillary sentinel lymph node biopsy with a radioisotope tracer. However, the patient opted for a conservative breast surgery and lymph node evaluation without tracer use. Following a comprehensive risk explanation, we performed a partial mastectomy and axillary lymph node sampling during her 22nd week of pregnancy. Post-delivery, further SDM sessions were held to discuss adjuvant therapy. Although we recommended the prompt initiation of radiotherapy, the patient chose to postpone it to continue breastfeeding. After she stopped breastfeeding, radiotherapy commenced 6 weeks post-delivery(24 weeks after surgery). After the SDM sessions, the chosen course may not align with optimal health practices. Nevertheless, SDM remains crucial, particularly for pregnancy-related breast cancer, given the limited high- grade evidence for treatment approaches in such cases.

Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study.

BACKGROUND: Recognition of the inferior mesenteric artery (IMA) during colorectal cancer surgery is crucial to avoid intraoperative hemorrhage and define the appropriate lymph node dissection line. This retrospective feasibility study aimed to develop an IMA anatomical recognition model for laparoscopic colorectal resection using deep learning, and to evaluate its recognition accuracy and real-time performance. METHODS: A complete multi-institutional surgical video database, LapSig300 was used for this study. Intraoperative videos of 60 patients who underwent laparoscopic sigmoid colon resection or high anterior resection were randomly extracted from the database and included. Deep learning-based semantic segmentation accuracy and real-time performance of the developed IMA recognition model were evaluated using Dice similarity coefficient (DSC) and frames per second (FPS), respectively. RESULTS: In a fivefold cross-validation conducted using 1200 annotated images for the IMA semantic segmentation task, the mean DSC value was 0.798 (± 0.0161 SD) and the maximum DSC was 0.816. The proposed deep learning model operated at a speed of over 12 FPS. CONCLUSION: To the best of our knowledge, this is the first study to evaluate the feasibility of real-time vascular anatomical navigation during laparoscopic colorectal surgery using a deep learning-based semantic segmentation approach. This experimental study was conducted to confirm the feasibility of our model; therefore, its safety and usefulness were not verified in clinical practice. However, the proposed deep learning model demonstrated a relatively high accuracy in recognizing IMA in intraoperative images. The proposed approach has potential application in image navigation systems for unfixed soft tissues and organs during various laparoscopic surgeries.

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery.

BACKGROUND: Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an image-guided navigation system with total mesorectal excision. IMPACT OF INNOVATION: The impact of innovation is the development of a deep learning-based image-guided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. TECHNOLOGY, MATERIALS, AND METHODS: This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane was performed. Intraoperative images capturing the total mesorectal excision scene extracted from left colorectal laparoscopic resection videos were used as training data for the deep learning model. Six hundred annotation images were created from 32 videos, with 528 images in the training and 72 images in the test data sets. The experimental feasibility study was conducted at the Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan. Dice coefficient was used to evaluate semantic segmentation accuracy for areolar tissue. PRELIMINARY RESULTS: The developed semantic segmentation model helped locate and highlight the areolar tissue area in the total mesorectal excision plane. The accuracy and generalization performance of deep learning models depend mainly on the quantity and quality of the training data. This study had only 600 images; thus, more images for training are necessary to improve the recognition accuracy. CONCLUSION AND FUTURE DIRECTIONS: We successfully developed a total mesorectal excision plane image-guided navigation system based on an areolar tissue segmentation approach with high accuracy. This may aid surgeons in recognizing the total mesorectal excision plane for dissection.

Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.

BACKGROUND: Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. METHODS: This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. RESULTS: Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. CONCLUSIONS: To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

Development and Validation of a 3-Dimensional Convolutional Neural Network for Automatic Surgical Skill Assessment Based on Spatiotemporal Video Analysis.

Importance: A high level of surgical skill is essential to prevent intraoperative problems. One important aspect of surgical education is surgical skill assessment, with pertinent feedback facilitating efficient skill acquisition by novices. Objectives: To develop a 3-dimensional (3-D) convolutional neural network (CNN) model for automatic surgical skill assessment and to evaluate the performance of the model in classification tasks by using laparoscopic colorectal surgical videos. Design, Setting, and Participants: This prognostic study used surgical videos acquired prior to 2017. In total, 650 laparoscopic colorectal surgical videos were provided for study purposes by the Japan Society for Endoscopic Surgery, and 74 were randomly extracted. Every video had highly reliable scores based on the Endoscopic Surgical Skill Qualification System (ESSQS, range 1-100, with higher scores indicating greater surgical skill) established by the society. Data were analyzed June to December 2020. Main Outcomes and Measures: From the groups with scores less than the difference between the mean and 2 SDs, within the range spanning the mean and 1 SD, and greater than the sum of the mean and 2 SDs, 17, 26, and 31 videos, respectively, were randomly extracted. In total, 1480 video clips with a length of 40 seconds each were extracted for each surgical step (medial mobilization, lateral mobilization, inferior mesenteric artery transection, and mesorectal transection) and separated into 1184 training sets and 296 test sets. Automatic surgical skill classification was performed based on spatiotemporal video analysis using the fully automated 3-D CNN model, and classification accuracies and screening accuracies for the groups with scores less than the mean minus 2 SDs and greater than the mean plus 2 SDs were calculated. Results: The mean (SD) ESSQS score of all 650 intraoperative videos was 66.2 (8.6) points and for the 74 videos used in the study, 67.6 (16.1) points. The proposed 3-D CNN model automatically classified video clips into groups with scores less than the mean minus 2 SDs, within 1 SD of the mean, and greater than the mean plus 2 SDs with a mean (SD) accuracy of 75.0% (6.3%). The highest accuracy was 83.8% for the inferior mesenteric artery transection. The model also screened for the group with scores less than the mean minus 2 SDs with 94.1% sensitivity and 96.5% specificity and for group with greater than the mean plus 2 SDs with 87.1% sensitivity and 86.0% specificity. Conclusions and Relevance: The results of this prognostic study showed that the proposed 3-D CNN model classified laparoscopic colorectal surgical videos with sufficient accuracy to be used for screening groups with scores greater than the mean plus 2 SDs and less than the mean minus 2 SDs. The proposed approach was fully automatic and easy to use for various types of surgery, and no special annotations or kinetics data extraction were required, indicating that this approach warrants further development for application to automatic surgical skill assessment.

[Neoadjuvant Chemoradiotherapy for Locally Advanced Carcinoma Associated with Anal Fistula].

The patient was a 59-year-old man. He was admitted to our hospital because of increasing anal pain with induration of the perianal region. There were large secondary orifices with mucous discharge on the left side of the perineal resion and buttock. We diagnosed adenocarcinoma on analysis of a biopsy specimen from induration of the perianal region. Pelvic CT and MRI showed that the tumor spreaded within the pelvis, with invasion of the prostate and sacrum. We performed neoadjuvant chemoradiotherapy preoperatively. After chemoradiotherapy, the tumor reduced in size greatly. We performed abdominoperineal resection and massive resection of skin of the perianal region. The defect of the pelvic floor and perianal skin was repaired using skin flap. The surgical margin was tumor free. Neoadjuvant chemoradiotherapy was considered effective for locally advanced carcinoma associated with anal fistula.

[Malignant Pleural Mesothelioma with Excessive Inflammation and High Level of Interleukin 6 - A Case Report].

Malignant mesothelioma is a rare aggressive solid tumor that is invariably incurable. A 23-year-old female patient with ascites, anemia, and high levels of ferritin and CRP was diagnosed with pleural mesothelioma by exploratory laparotomy. She remained asymptomatic, but 7 years later, she developed intractable diarrhea and fever. Systematic chemotherapy with both cisplatin and pemetrexed was administered. However, the treatment was discontinued due to side effects, after which time the diarrhea, ascites, and fever became progressively more severe. Hepatomegaly and hepatic siderosis also developed. At the same time, the patient's serum interleukin 6(IL-6)levels were abnormally high. Although there was a temporary symptomatic improvement after intraperitoneal injection of cisplatin, the intractable mesothelioma-associated symptoms returned a few days later. The patient died of liver failure 1 week later. The poor prognosis in this case was due to symptoms associated with the high IL-6 level. There are limited medically proven treatments, and it is important to develop new treatments. Therefore, "anti-IL-6 therapy" may have to be tested as a potential treatment for symptoms associated with high IL-6 levels.

Structural and functional mosaic nature of MHC class I molecules in their peptide-free form.

Despite well-organized peptide-loading mechanisms within the endoplasmic reticulum, major histocompatibility complex class I (MHC-I) molecules can be displayed on cell surfaces in peptide-free forms. Although these empty MHC-I (eMHC-I) molecules are presumably involved in physiological and pathological processes, little is known about their structures and functions due to their instability. Using bacterially expressed HLA-Cw*07:02 heavy chain and ß2 microglobulin molecules, we successfully established an in vitro refolding method to prepare eMHC-I molecules in sufficient quantities for detailed structural analyses. NMR spectroscopy in conjunction with subunit-specific ¹5N-labeling techniques revealed that the peptide-binding domains and the adjacent regions were unstructured in the peptide-free form, while the remaining regions maintained their structural integrity. Consistent with our spectroscopic data, the eMHC-I complex could interact with leukocyte Ig-like receptor B1, but not with killer cell Ig-like receptor 2DL3. Thus, eMHC-I molecules have a mosaic nature in terms of their three-dimensional structure and binding to immunologically relevant molecules.