RESUMO
BACKGROUND: In laparoscopic right hemicolectomy (RHC) for right-sided colon cancer, accurate recognition of the vascular anatomy is required for appropriate lymph node harvesting and safe operative procedures. We aimed to develop a deep learning model that enables the automatic recognition and visualization of major blood vessels in laparoscopic RHC. MATERIALS AND METHODS: This was a single-institution retrospective feasibility study. Semantic segmentation of three vessel areas, including the superior mesenteric vein (SMV), ileocolic artery (ICA), and ileocolic vein (ICV), was performed using the developed deep learning model. The Dice coefficient, recall, and precision were utilized as evaluation metrics to quantify the model performance after fivefold cross-validation. The model was further qualitatively appraised by 13 surgeons, based on a grading rubric to assess its potential for clinical application. RESULTS: In total, 2624 images were extracted from 104 laparoscopic colectomy for right-sided colon cancer videos, and the pixels corresponding to the SMV, ICA, and ICV were manually annotated and utilized as training data. SMV recognition was the most accurate, with all three evaluation metrics having values above 0.75, whereas the recognition accuracy of ICA and ICV ranged from 0.53 to 0.57 for the three evaluation metrics. Additionally, all 13 surgeons gave acceptable ratings for the possibility of clinical application in rubric-based quantitative evaluations. CONCLUSION: We developed a DL-based vessel segmentation model capable of achieving feasible identification and visualization of major blood vessels in association with RHC. This model may be used by surgeons to accomplish reliable navigation of vessel visualization.
Assuntos
Neoplasias do Colo , Aprendizado Profundo , Laparoscopia , Humanos , Neoplasias do Colo/diagnóstico por imagem , Neoplasias do Colo/cirurgia , Neoplasias do Colo/irrigação sanguínea , Estudos Retrospectivos , Laparoscopia/métodos , Colectomia/métodosRESUMO
PURPOSE: Laparoscopic distal gastrectomy (LDG) is a difficult procedure for early career surgeons. Artificial intelligence (AI)-based surgical step recognition is crucial for establishing context-aware computer-aided surgery systems. In this study, we aimed to develop an automatic recognition model for LDG using AI and evaluate its performance. METHODS: Patients who underwent LDG at our institution in 2019 were included in this study. Surgical video data were classified into the following nine steps: (1) Port insertion; (2) Lymphadenectomy on the left side of the greater curvature; (3) Lymphadenectomy on the right side of the greater curvature; (4) Division of the duodenum; (5) Lymphadenectomy of the suprapancreatic area; (6) Lymphadenectomy on the lesser curvature; (7) Division of the stomach; (8) Reconstruction; and (9) From reconstruction to completion of surgery. Two gastric surgeons manually assigned all annotation labels. Convolutional neural network (CNN)-based image classification was further employed to identify surgical steps. RESULTS: The dataset comprised 40 LDG videos. Over 1,000,000 frames with annotated labels of the LDG steps were used to train the deep-learning model, with 30 and 10 surgical videos for training and validation, respectively. The classification accuracies of the developed models were precision, 0.88; recall, 0.87; F1 score, 0.88; and overall accuracy, 0.89. The inference speed of the proposed model was 32 ps. CONCLUSION: The developed CNN model automatically recognized the LDG surgical process with relatively high accuracy. Adding more data to this model could provide a fundamental technology that could be used in the development of future surgical instruments.
Assuntos
Inteligência Artificial , Gastrectomia , Laparoscopia , Estudo de Prova de Conceito , Neoplasias Gástricas , Humanos , Gastrectomia/métodos , Laparoscopia/métodos , Neoplasias Gástricas/cirurgia , Neoplasias Gástricas/patologia , Feminino , Masculino , Pessoa de Meia-Idade , Cirurgia Assistida por Computador/métodos , Idoso , Excisão de LinfonodoRESUMO
To prevent intraoperative organ injury, surgeons strive to identify anatomical structures as early and accurately as possible during surgery. The objective of this prospective observational study was to develop artificial intelligence (AI)-based real-time automatic organ recognition models in laparoscopic surgery and to compare its performance with that of surgeons. The time taken to recognize target anatomy between AI and both expert and novice surgeons was compared. The AI models demonstrated faster recognition of target anatomy than surgeons, especially novice surgeons. These findings suggest that AI has the potential to compensate for the skill and experience gap between surgeons.
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Cirurgia Colorretal , Procedimentos Cirúrgicos do Sistema Digestório , Laparoscopia , Humanos , Inteligência ArtificialRESUMO
OBJECTIVE: To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions. METHODS: Intraoperative videos of robot-assisted radical prostatectomy performed by the posterior approach from 3 institutions were obtained between 2019 and 2020. Using SV-VD dissection videos, semantic segmentation of the seminal vesicle-vas deferens area was performed using a convolutional neural network-based approach. The dataset was split into training and test data in a 10:3 ratio. The average time required by 6 novice urologists to correctly recognize the SV-VD was compared using intraoperative videos with and without segmentation masks generated by the convolutional neural network model, which was evaluated with the test data using the Dice similarity coefficient. Training and test datasets were compared using the Mann-Whitney U-test and chi-square test. Time required to recognize the SV-VD was evaluated using the Mann-Whitney U-test. RESULTS: From 26 patient videos, 1 040 images were created (520 SV-VD annotated images and 520 SV-VD non-displayed images). The convolutional neural network model had a Dice similarity coefficient value of 0.73 in the test data. Compared with original videos, videos with the generated segmentation mask promoted significantly faster seminal vesicle and vas deferens recognition (P < .001). CONCLUSION: The convolutional neural network model provides accurate recognition of the SV-VD in the posterior approach RARP, which may be helpful, especially for novice urologists.
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Aprendizado Profundo , Robótica , Masculino , Humanos , Glândulas Seminais , Ducto Deferente , Prostatectomia/métodos , Processamento de Imagem Assistida por ComputadorRESUMO
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.
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Cirurgia Colorretal , Aprendizado Profundo , Laparoscopia , Humanos , Projetos Piloto , Semântica , Vias Autônomas/cirurgia , Laparoscopia/métodosRESUMO
BACKGROUND AND OBJECTIVE: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. METHODS: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. RESULTS: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). CONCLUSION: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.
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Algoritmos , Procedimentos Cirúrgicos Robóticos , Humanos , Fluxo de Trabalho , Procedimentos Cirúrgicos Robóticos/métodosRESUMO
Drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms is a life-threatening severe cutaneous adverse reaction, characterized by multiple organ involvement and reactivation of herpes viruses. Although the mainstay of treatment is a high dosage of corticosteroids delivered by pulse therapy or conventional oral administration, it remains debatable which mode is better. To clarify this issue, we reviewed publications in Japan of 299 cases of drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms treated with corticosteroids, to evaluate safety concerns with regards to these two modes of treatment. As a result, we found that patients treated with pulse therapy more frequently suffered cytomegalovirus reactivation, persistency, and high mortality but less frequently experienced herpesvirus 6 reactivation or type 1 diabetes compared with those undergoing conventional treatment, suggesting that the administration mode may differentially modulate inflammatory responses toward distinct consequences. This is the first statistical analysis revealing that steroid pulse therapy frequently resulted in severe sequelae with high mortality. In terms of the risk of serious consequences, we consider that steroid pulse therapy should be eschewed for the treatment of drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms.
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Síndrome de Hipersensibilidade a Medicamentos , Preparações Farmacêuticas , Corticosteroides/efeitos adversos , Síndrome de Hipersensibilidade a Medicamentos/diagnóstico , Síndrome de Hipersensibilidade a Medicamentos/epidemiologia , Síndrome de Hipersensibilidade a Medicamentos/etiologia , Humanos , Japão/epidemiologia , EsteroidesRESUMO
Immunoglobulin (Ig)A vasculitis/nephropathy is a systemic immune complex-mediated vasculitis. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination is widely recommended in individuals without specific allergy to the vaccine components, it is arguable whether vaccination is advisable for patients with IgA vasculitis or for predisposed individuals. We and others have presented cases of IgA vasculitis occurring after SARS-CoV-2 vaccination. In total, these 19 cases, including ours, involved predominantly female patients, and half of them were suffering from de novo vasculitis onset. The most frequent manifestation was gross hematuria (89.5%) while skin lesions were relatively infrequent, occurring in only five cases (26.3%), of which three (15.8%) were confirmed to be IgA vasculitis. Taken together, these cases suggest that SARS-CoV-2 vaccination might be a trigger for development/deterioration of IgA vasculitis/nephropathy.
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COVID-19 , Glomerulonefrite por IGA , Vasculite por IgA , COVID-19/prevenção & controle , Vacinas contra COVID-19/efeitos adversos , Feminino , Glomerulonefrite por IGA/etiologia , Humanos , Imunoglobulina A , Masculino , SARS-CoV-2 , Vacinação/efeitos adversosRESUMO
Nucleophosmin 1 (NPM1) primarily localizes to the nucleus and is passively released into the extracellular milieu by necrotic or damaged cells, or is secreted by monocytes and macrophages. Extracellular NPM1 acts as a potent inflammatory stimulator by promoting cytokine production [e.g., tumor necrosis factor-α (TNF-α)], which suggests that NPM1 acts as a damage-associated molecular pattern. However, the receptor of NPM1 is unknown. Evidence indicates that DAMPs, which include high mobility group box 1 and histones, may bind Toll-like receptors (TLRs). In the present study, it was shown that NPM1 signaling was mediated via the TLR4 pathway, which suggests that TLR4 is an NPM1 receptor. TLR4 binds myeloid differentiation protein-2 (MD-2), which is essential for intracellular signaling. Furthermore, the TLR4 antagonist, LPS-Rhodobacter sphaeroides (an MD-2 antagonist) and TAK-242 (a TLR4 signaling inhibitor) significantly inhibited NPM1-induced TNF-α production by differentiated THP-1 cells as well as reducing ERK1/2 activation. Far-western blot analysis revealed that NPM1 directly bound MD-2. Thus, the results of the present study provide compelling evidence that TLR4 binds NPM1, and it is hypothesized that inhibiting NPM1 activity may serve as a novel strategy for treating TLR4-related diseases.