Objective:To explore the clinical application value of artificial intelligence models in identifying bleeding events and hemorrhagic points during laparoscopic pancreatic surgery.Methods:This single-center retrospective cohort study collected surgical videos of 25 patients undergoing laparoscopic pancreatic surgery at the Department of General Surgery, Peking Union Medical College Hospital from January 2022 to December 2024. Videos within 5 seconds before and after representative bleeding events were captured at 30 frames/s, with 11 666 hemorrhagic-related video frames annotated. Two algorithm models were developed: a pigment-based model and a pigment+optical flow-based model for classification and target recognition of bleeding frames. The training and test sets for the pigment-based algorithm contained 4 692 hemorrhagic and 4 309 non-hemorrhagic frames, while those for the pigment+optical flow model included 1 339 hemorrhagic and 1 326 non-hemorrhagic frames. Performance evaluation was conducted using overlap thresholds, with accuracy and recall rates as key metrics.Results:The pigment-based model achieved 93.8% accuracy (134/143) and 43.3% recall (134/310) in hemorrhagic frame classification. At an overlap threshold of 0.3, the pigment-based model showed 84.1% accuracy (433/515) and 85.4% recall (433/507) in target recognition. When the threshold was increased to 0.5, the pigment+optical flow model demonstrated 88.1% accuracy (354/402) and 89.2% recall (354/397) in hemorrhagic target recognition.Conclusions:It is difficult to distinguish active bleeding from old bleeding completely by pigment information alone. The spatio-temporal features can be effectively extracted by combining pigment and optical flow information, and the bleeding can be accurately identified and located, which has potential clinical application value.

Identification and functional protection of parathyroid glands are the key to reduce the incidence of postoperative complications after thyroid surgery. In recent years, the development of several fluorescence imaging technology and the application of artificial intelligence based on deep learning in thyroid surgery have brought technical breakthroughs in the identification and blood supply assessment of parathyroid glands during surgery, helping surgeons to identify parathyroid glands quickly and accurately, and improving the prognosis of surgery. Based on this, this article focuses on the new advances in the identification and protection of parathyroid glands in thyroid surgery, especially the research and application progress of fluorescence imaging, lymph node tracers, artificial intelligence and other aspects, and discusses the future development prospects.

Identification and functional protection of parathyroid glands are the key to reduce the incidence of postoperative complications after thyroid surgery. In recent years, the development of several fluorescence imaging technology and the application of artificial intelligence based on deep learning in thyroid surgery have brought technical breakthroughs in the identification and blood supply assessment of parathyroid glands during surgery, helping surgeons to identify parathyroid glands quickly and accurately, and improving the prognosis of surgery. Based on this, this article focuses on the new advances in the identification and protection of parathyroid glands in thyroid surgery, especially the research and application progress of fluorescence imaging, lymph node tracers, artificial intelligence and other aspects, and discusses the future development prospects.

Objective:To explore the clinical application value of artificial intelligence models in identifying bleeding events and hemorrhagic points during laparoscopic pancreatic surgery.Methods:This single-center retrospective cohort study collected surgical videos of 25 patients undergoing laparoscopic pancreatic surgery at the Department of General Surgery, Peking Union Medical College Hospital from January 2022 to December 2024. Videos within 5 seconds before and after representative bleeding events were captured at 30 frames/s, with 11 666 hemorrhagic-related video frames annotated. Two algorithm models were developed: a pigment-based model and a pigment+optical flow-based model for classification and target recognition of bleeding frames. The training and test sets for the pigment-based algorithm contained 4 692 hemorrhagic and 4 309 non-hemorrhagic frames, while those for the pigment+optical flow model included 1 339 hemorrhagic and 1 326 non-hemorrhagic frames. Performance evaluation was conducted using overlap thresholds, with accuracy and recall rates as key metrics.Results:The pigment-based model achieved 93.8% accuracy (134/143) and 43.3% recall (134/310) in hemorrhagic frame classification. At an overlap threshold of 0.3, the pigment-based model showed 84.1% accuracy (433/515) and 85.4% recall (433/507) in target recognition. When the threshold was increased to 0.5, the pigment+optical flow model demonstrated 88.1% accuracy (354/402) and 89.2% recall (354/397) in hemorrhagic target recognition.Conclusions:It is difficult to distinguish active bleeding from old bleeding completely by pigment information alone. The spatio-temporal features can be effectively extracted by combining pigment and optical flow information, and the bleeding can be accurately identified and located, which has potential clinical application value.

In recent years, artificial intelligence technology has been empowering various industries and leading industrial upgrading. The progress of artificial intelligence in medical image analysis and surgical navigation positioning is revolutionizing the entire medical field and gradually penetrating into the diagnosis and treatment of thyroid and parathyroid diseases. This article focuses on the application of artificial intelligence in the surgical diagnosis and treatment of thyroid and parathyroid diseases, emphasizing the research and application progress of deep learning based artificial intelligence systems in preoperative evaluation, intraoperative decision-making assistance, and postoperative prognosis prediction, and exploring future development prospects.

Objective:To explore the value of mitoxantrone hydrochloride injection for tracing in endoscopic thyroidectomy (ETE) via anterior chest approach for papillary thyroid carcinoma (PTC) .Methods:A retrospective analysis was conducted on patients undergoing ETE via anterior chest approach for PTC admitted to Beijing Longfu Hospital (Medical Treatment Combination with Peking Union Medical College Hospital) from Sep. 2022 to Mar. 2024. The patients were divided into two groups: the control group (without tracer) and the tracer group (with mitoxantrone hydrochloride injection for tracing). All surgeries were performed by the same thyroid surgical team. Baseline, postoperative pathologies and complications were compared between the 2 groups.Results:A total of 25 patients (13 in the control group and 12 in the tracer group) were included in this study, and the average dissection of unilateral central region lymph nodes in the tracer group was 7.4±4.6, significantly more than in the control group (2.4±1.9) ( P=0.004). There were no instances of mistakenly resected parathyroid gland in the postoperative pathology or accidental injury of recurrent laryngeal nerve in either group. The incidence of transient hypocalcemia did not significantly different between the two groups ( P=0.503). However, the incidence of transient hypoparathyroidism in the tracer group was 1 (1/12,8.3%), significantly lower than in the control group 4 (4/13,30.8%) ( P=0.009). The tracer group exhibited more impressive levels in parathyroid hormone (5.4±8.1) pg/mL compared to the control group (20.0±11.1) pg/mL ( P=0.001) .The total volume of postoperative drainage in the tracer group (142.9±71.7) mL was more than that of the control group (87.7±38.8) mL ( P=0.030). But It did not affect the extubation time in either group ( P=0.610). No residual tracer was observed at the skin puncture site in the tracer group after 2 weeks. Conclusions:Mitoxantrone hydrochloride injection for tracing as tracer in ETE via breast approach can increase the number of pathological lymph nodes dissection in cervical central region. Combined with negative development, identifying and protecting the function of parathyroid glands show feasible and potential application value to improve the safety of thyroidectomy. The use of mitoxantrone hydrochloride injection for tracer has the risk of increased exudation from the surgical area, but does not affect the time to remove the drain.

Objective:To explore the use of near-infrared autofluorescence probe (NIRAF-P) and its application in identifying parathyroid glands during surgery.Methods:A total of 68 patients undergoing thyroid surgery at Peking Union Medical College Hospital and Beijing Longfu Hospital between Dec. 2023 and Jun. 2024 were selected. During the operation, the near-infrared parathyroid gland detector was used to identify the parathyroid gland tissue to be tested, and histopathological examination was performed. The positive predictive value and accuracy of the near-infrared parathyroid gland detector were analyzed.Results:A total of 111 parathyroid glands were identified in 68 patients, and the positive predictive value and accuracy of the NIRAF-P were 95.5% and 94.6%, respectively.Conclusions:The NIRAF-P has high accuracy in identifying parathyroid glands. The standardized application of the NIRAF-P can help improve the efficiency of identifying parathyroid glands during surgery.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To explore the feasibility of deep learning technology for renal artery recognition in retroperitoneal laparoscopic renal surgery videos.Methods:From January 2020 to July 2021, the video data of 87 cases of laparoscopic retroperitoneal nephrectomy, including radical nephrectomy, partial nephrectomy, and hemiurorectomy, were retrospectively analyzed. Two urological surgeons screened video clips containing renal arteries. After frame extraction, annotation, review, and proofreading, the labeled targets were divided into training set and test set by the random number table in a ratio of 4∶1. The training set was used to train the neural network model. The test set was used to test the ability of the neural network to identify the renal artery in scenes with different difficulties, which was uniformly transmitted to the YOLOv3 convolutional neural network model for training. According to the opinion of two senior doctors, the test set was divided into high, medium, and low discrimination of renal artery and surrounding tissue. High identification means a clean renal artery and a large exposed area. For middle recognition degree, the renal artery had a certain degree of blood immersion, and the exposed area was medium. Low identification means that the exposed area of the renal artery was small, often located at the edge of the lens, and the blood immersion was severe, which may lead to lens blurring. In the surgical video, the annotator annotated the renal artery truth box frame by frame. After normalization and preprocessing, all images were input into the neural network model for training. The neural network output the renal artery prediction box, and if the overlap ratio (IOU) with the true value box was higher than the set threshold, it was judged that the prediction was correct. The neural network test results of the test set were recorded, and the sensitivity and accuracy were calculated according to IOU.Results:In the training set, 1 149 targets of 13 videos had high recognition degree, 1 891 targets of 17 videos had medium recognition degree, and 349 targets of 18 videos had low recognition degree. In the test set, 267 targets in 9 videos had high recognition degree, 519 targets in 11 videos had medium recognition degree, and 349 targets in 18 videos had low recognition degree. When the IOU threshold was 0.1, the sensitivity and accuracy were 52.78% and 82.50%, respectively. When the IOU threshold was 0.5, the sensitivity and accuracy were 37.80% and 59.10%, respectively. When the IOU threshold was 0.1, the sensitivity and accuracy of high, medium and low recognition groups were 89.14% and 87.82%, 45.86% and 78.03%, 32.95%, and 76.67%, respectively. The frame rate of the YOLOv3 algorithm in real-time surgery video was ≥15 frames/second. The false detection rate and missed detection rate of neural network for renal artery identification in laparoscopic renal surgery video were 47.22% and 17.49%, respectively (IOU=0.1). The leading causes of false detection were similar tissue and reflective light. The main reasons for missed detection were image blurring, blood dipping, dark light, fascia interference, or instrument occlusion, etc.Conclusions:Deep learning-based renal artery recognition technology is feasible. It may assist the surgeon in quickly identifying and protecting the renal artery during the operation and improving the safety of surgery.

Objective:To explore whether deep learning could apply to recognize the recurrent laryngeal nerve (RLN) in videos of endoscopic thyroidectomy (ETE) via breast approach.Methods:Videos of ETE via breast approach in Peking Union Medical College Hospital from Feb. 2020 to Aug. 2021 were collected. Videos containing RLN were selected, and the outline of RLN was marked by two thyroid surgeons. Then data were divided into a training set and a test set in a ratio of 5:1 and classified into the high and low difficulty group according to a senior thyroid surgeon’s opinion. Those pictures were input to D-LinkNet model. Precision, sensitivity and mean dice index was calculated.Results:A total of 46 videos including 153, 520 frames of pictures were included in this study. 131,039 frames of 39 videos were in the training set and 22,481 frames of 7 videos were in the test set. When the intersection over union threshold was 0.1, the sensitivity and precision was 92.9%/72.8% and 47.6%/54.9% in high and low recognition group, respectively. When the intersection over union threshold was 0.5, the sensitivity and precision turned to 85.8%/67.2% and 37.6%/43.5% in high and low difficulty group, respectively. Mean Dice index was 0.781 and 0.663 in high and low difficulty group, respectively.Conclusions:RLN recognition based on deep learning is feasible and has potential application value in ETE, which may help surgeons reduce the risk of accidental injury of RLN and improve the safety of thyroidectomy.