ABSTRACT
ObjectiveTo introduce a fixation technique with the modified levonorgestrel-releasing intrauterine system （LNG-IUS） and evaluate its efficacy in the treatment of adenomyosis patients with previous LNG-IUS expulsion. MethodsA retrospective analysis was done on 22 adenomyosis patients who underwent modified LNG-IUS fixation due to LNG-IUS expulsion at three hospitals from June 2022 to June 2023. The baseline clinical characteristics， operative and postoperative details were collected and analyzed. The Visual analogu scale （VAS） scores and pictorial blood loss assessment chart （PBAC） scores were measured and compared before， 3 and 6 months after the LNG-IUS fixation. ResultsThe mean operative time was （19.51±7.41） min and intraoperative bleeding was （6.71±5.30） mL. Of the patients， 13 were operated under local anaesthesia and the other 9 under intravenous anaesthesia. There were 4 operations performed by a resident doctor， 15 by an attending doctor and 3 by a senior doctor. No intraoperative or postoperative complication was found. The mean follow-up was 11.51 months and no patient had a recurrence of LNG-IUS expulsion during the follow-up period. The mean level of hemoglobin at 1 month after operation was significantly higher than that before （P<0.001）. VAS scores and PBAC scores at 3 and 6 months postoperatively were all improved significantly than those preoperatively （P<0.001）. ConclusionsEffectively preventing the recurrence of LNG-IUS expulsion， modified LNG-IUS fixation is a safe and efficient method for adenomyosis patients with previous LNG-IUS expulsion. Modified LNG-IUS fixation deserves the clinical application due to its easy operation and wide range of use on women.
ABSTRACT
Objective:To establish an artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning optical coherence tomography (OCT) and evaluate its application value.Methods:Diagnostic test studies. From 2016 to 2019, 25 000 OCT images of 25 000 patients treated at the Eye Center of the Second Affiliated Hospital of Zhejiang University School of Medicine were used as training sets and validation sets for the fundus intelligent assisted diagnosis system. Among them, macular epiretinal membrane (MERM), macular edema, macular hole, choroidal neovascularization (CNV), and age-related macular degeneration (AMD) were 5 000 sheets each. The training set and the verification set are 18 124 and 6 876 sheets, respectively. Through the transfer learning Attention ResNet structure algorithm, the OCT image was characterized by lesion identification, the disease feature was extracted by a specific procedure, and the given image was distinguished from other types of disease according to the statistical characteristics of the target lesion. The model algorithms of MERM, macular edema, macular hole, CNV and AMD were initially formed, and the fundus intelligent auxiliary diagnosis system of five models was established. The performance of each model-assisted diagnosis in the fundus intelligent auxiliary diagnostic system was evaluated by applying the subject working characteristic curve, area under the curve (AUC), sensitivity, and specificity.Results:With the intelligent auxiliary diagnosis system, the diagnostic sensitivity of the MERM was 93.5%, the specificity was 99.23%, and AUC was 0.983 7; the diagnostic sensitivity of macular edema was 99.02%, the specificity was 98.17%, and AUC was 0.994 6; the diagnostic sensitivity of macular hole was 98.91%, the specificity was 99.91%, AUC was 0.996 2; the diagnostic sensitivity of CNV was 97.54%, the specificity was 94.71%, AUC was 0.987 5; the diagnostic sensitivity of AMD was 95.12%, the specificity was 97.09%, AUC was 0.985 3.Conclusions:The artificial intelligence robot-assisted diagnosis system for fundus diseases based on deep learning for OCT images has accurate and efficient diagnostic performance for assisting the diagnosis of MERM, macular edema, macular hole, CNV, and AMD.
ABSTRACT
Objective To study the problems in clinical practice of nursing students and their experience in the operating room.Methods Interview,on-the-spot recording and note-taking were used in the investigation among 16 nursing students at their clinic internship in the operating room.The data were analyzed by way of type analyzing method.Result Four factors were found, including un-adaptability to the clinical environment,distance between nursing students and teachers,lack of high-tech,and emotional experience.Conclusion The nursing teachers should make clear teaching aim,reduce their mistakes in the work in the operation room,and attach more importance to the cultivation of humanistic quality and interpersonal communications.