Experienced Doctors Research Articles

Point-of-care ultrasound of the inflammatory bowel disease

Background. The widespread use of portable ultrasound scanners has advanced the concept of ultrasound diagnosis (POCUS), namely «ultrasound examination (US) is performed at the bedside and is interpreted directly by a physician». Pocus is not a substitute for complex ultrasound, but rather allows the ultrasound doctor to quickly access clinical images for rapid diagnosis and effective examination and treatment of patients. Purpose – the work to identify ultrasound signs of inflammatory bowel disease (IBD) using POCUS. Materials and Methods. 70 patients aged 18 to 39 years (24 men and 46 women, mean age – 30.3 ± 13.7 years). The first group of patients included 32 people with a morphologically confirmed CD diagnosis, the second group included 38 people with a morphologically confirmed UC diagnosis. The control group consisted of 2 8 patients with no clinical and laboratory data on gastrointestinal lesions. Results. It was shown that the ultrasound can assess the thickness of the intestinal wall with sensitivity – 90.3% and specificity – 87.1% and ROC – 0.882, differentiation of the wall layers, surrounding structures (omentum, mesentery, lymph nodes), as well as the localization of the affected intestinal segment. Color Doppler Mapping (CDM) method with a sensitivity of 86.5% and specificity of 81.5% and ROC of 0.852 allows not only to assume the presence of active inflammation in the wall of the affected intestinal segment, but also to monitor the dynamics of the process during treatment. Conclusion. In this work, the capabilities of POCUS for the diagnosis of IBD were evaluated. Correct and accurate interpretation of POCUS findings is not only an important diagnostic step, but also a complement to other radiation and endoscopic imaging techniques. The main advantage of POCUS over CT and MRI is its fast availability, low cost, and high level of safety. An experienced ultrasound doctor is needed to examine the gastrointestinal tract, so training with ultrasound doctors and mastering practical skills is key to ensuring the effective use of POCUS.

Satisfaction of patients with frozen shoulder following silent manipulation: a prospective observation study.

Silent manipulation is a procedure for frozen shoulders that involves manipulating the shoulder while the patient is awake by performing C5, C6, and C7 cervical nerve root block under ultrasound guidance. This retrospective study, conducted at Yokohama City University Hospital, aimed to evaluate the clinical outcomes of silent manipulation and assess whether the experience level of the practitioner influenced treatment efficacy. Between October 2020 and January 2022, 53 patients who met the inclusion criteria underwent silent manipulation for frozen shoulder. The procedure was performed by either an experienced or a less experienced practitioner, and the patients were followed-up for up to 1year post-treatment. Silent manipulation resulted in significant improvements in shoulder range of motion, as measured by forward flexion, abduction, external rotation, and hand-behind-back, as well as in patient-reported outcomes, including disabilities of the arm, shoulder, and hand and Shoulder 36 scores. These improvements were observed 1week, 3months, and 1year after silent manipulation, indicating the short-term efficacy of the procedure. Furthermore, this study revealed that the practitioners' level of experience played a significant role in the outcomes. The experienced doctor achieved better 1st external rotation and belt tying outcomes, as well as Shoulder 36 pain, muscle strength, and activities of daily living domain scores. This suggests that technical expertise in silent manipulation is crucial to achieve optimal outcomes. Silent manipulation offers an effective therapeutic approach for frozen shoulder, leading to significant improvements in range of motion and patient satisfaction. Practitioner expertise is a vital factor in treatment success, emphasizing the importance of skilled professionals in the performance of this procedure.

Robotic-assisted CT-guided percutaneous pulmonary nodules localization by hook-wire needles: a retrospective observational study.

Preoperative computed tomography (CT)-guided localization of small pulmonary nodules (SPNs) is the major approach for accurate intraoperative visualization in video-assisted thoracoscopic surgery (VATS). However, this interventional procedure has certain risks and may challenge to less experienced junior doctors. This study aims to evaluate the feasibility and efficacy of robotic-assisted CT-guided preoperative pulmonary nodules localization with the modified hook-wire needles before VATS. A total of 599 patients with 654 SPNs who preoperatively accepted robotic-assisted CT-guided percutaneous pulmonary localization were respectively enrolled and compared to 90 patients with 94 SPNs who underwent the conventional CT-guided manual localization. The clinical and imaging data including patients' basic information, pulmonary nodule features, location procedure findings, and operation time were analyzed. The localization success rate was 96.64% (632/654). The mean time required for marking was 22.85±10.27 min. Anchor of dislodgement occurred in 2 cases (0.31%). Localization-related complications included pneumothorax in 163 cases (27.21%), parenchymal hemorrhage in 222 cases (33.94%), pleural reaction in 3 cases (0.50%), and intercostal vascular hemorrhage in 5 cases (0.83%). Localization and VATS were performed within 24 hours. All devices were successfully retrieved in VATS. Histopathological examination revealed 166 (25.38%) benign nodules and 488 (74.62%) malignant nodules. For patients who received localizations, VATS spent a significantly shorter time, especially the segmentectomy group (93.61±35.72 vs. 167.50±40.70 min, P<0.001). The proportion of pneumothorax in the robotic-assisted group significantly decreased compared with the conventional manual group (27.21% vs. 43.33%, P=0.002). Robotic-assisted CT-guided percutaneous pulmonary nodules hook-wire localization could be effectively helpful for junior less experienced interventional physicians to master the procedure and potentially increase precision.

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

BackgroundAs global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.MethodsIn this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.ResultsThe use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.ConclusionThe diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

Context of Saudi Arabia: Does Education Influence Effective Communication amongst Individuals?

Education is critical for learning how to communicate with others. Through formal schooling, people learn the language (how to read and write) and interact with others in the same setting. One of the study's assumptions is that well-educated people communicate well, leading to better jobs and higher income. Another assumption is that communication skills improve with age due to the practicality of employing what has been taught on a daily basis, especially among professionals. An experienced medical doctor, journalist, or educator has enhanced communication skills compared to entrants within the same profession. Also, people with any formal career in the sector have better communication skills than illiterate individuals or those with elementary-level education. In Addition, the chance to apply one's skills can make a difference in people's communication skills in varying careers. For these reasons, respondents' level of education was analyzed with monthly income and work sector. The chi-square test was applied, and it was found that there is a significant relationship between the level of education and monthly income and age group but now work sector. One possible reason for the latter finding is that respondents' careers and professions were not specified. Despite these findings, there are variables to consider among individuals when assessing their education and communication skills such as their experience and whether they are self-taught.

Where do ICU trainees really look? An eye-tracking analysis of gaze patterns during central venous catheter insertion

Background: There is limited knowledge about gaze patterns of intensive care unit (ICU) trainee doctors during the insertion of a central venous catheter (CVC). The primary objective of this study was to examine visual patterns exhibited by ICU trainee doctors during CVC insertion. Additionally, the study investigated whether differences in gaze patterns could be identified between more and less experienced trainee doctors. Methods: In a real-life, prospective observational study conducted at the interdisciplinary ICU at the University Hospital Zurich, Switzerland, ICU trainee doctors underwent eye-tracking during CVC insertion in a real ICU patient. Using mixed-effects model analyses, the primary outcomes were dwell time, first fixation duration, revisits, fixation count, and average fixation time on different areas of interest (AOI). Secondary outcomes were above eye-tracking outcome measures stratified according to experience level of participants. Results: Eighteen participants were included, of whom 10 were inexperienced and eight more experienced. Dwell time was highest for CVC preparation table ( p = 0.02), jugular vein on ultrasound image ( p &lt; 0.001) and cervical puncture location ( p &lt; 0.001). Concerning experience, dwell time and revisits on jugular vein on ultrasound image ( p = 0.02 and p = 0.04, respectively) and cervical puncture location ( p = 0.004 and p = 0.01, respectively) were decreased in more experienced ICU trainees. Conclusions: Various AOIs have distinct significance for ICU trainee doctors during CVC insertion. Experienced participants exhibited different gaze behavior, requiring less attention for preparation and handling tasks, emphasizing the importance of hand-eye coordination.

Health Seeking Patterns among Tribal Women in Lahaul Spiti and Kinnaur Districts of Himachal Pradesh: Addressing Healthcare Challenges

The present study investigates the health seeking behaviour of tribal women in the remote regions of Lahaul Spiti and Kinnaur. It emphasizes the role of supply side factors as key determinants in healthcare access. Despite governmental efforts to enhance healthcare reach, our study uncovers significant shortcomings that adversely affect service quality. Through primary survey and focus group discussions, the study explores the challenges faced by tribal women. These challenges include a constantly changing roster of medical professionals, limited access to experienced doctors, and a notable lack of specialized care, especially in gynaecology. The frequent dysfunction of vital diagnostic tools forces local residents to undertake long, costly journeys for medical care, leading to potential delays in receiving treatment. The study also observes the prevalent use of traditional medicine, deeply ingrained in local traditions, serving as both a supplement and an alternative to the mainstream healthcare system. This research sheds light on how these deficiencies in supply side healthcare, compounded by environmental and infrastructural barriers, influence the health related decisions of women, often pushing them towards alternative treatments and increasing their socio economic strain. The findings emphasize the urgent need for an integrated approach to bridge the healthcare gap and ensure the provision of quality medical services to these isolated and vulnerable communities.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Seizure stage detection of epileptic seizure using convolutional neural networks

According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

WET-UNet: Wavelet integrated efficient transformer networks for nasopharyngeal carcinoma tumor segmentation.

Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verification is 8:2. In the experiment, accuracy = 85.2% and precision = 84.9% can show that our proposed model has good performance in nasopharyngeal cancer image segmentation.

The impact of strategic knowledge on strategic improvisation: an exploratory study of some private hospitals in the city of Baghdad

The current study aimed to know the role that strategic knowledge plays on strategic improvisation in private hospitals (the Royal Hospital, Al-Salama Hospital, Al-Karkh Al-Ahli Hospital, and Al-Shifa Hospital). To achieve the objectives of the study, the descriptive analytical approach was adopted, where the researchers developed a questionnaire consisting of (30) items. A random sample was drawn so that the study sample consisted of (103) workers in private hospitals, and the results were analyzed based on the (SPSS) program and descriptive statistics tools. It was concluded that private hospitals currently have a great interest in the strategy of strategic knowledge and strategic improvisation, and provide services It is of high quality and attracts highly experienced doctors. Strategic knowledge is closely linked to strategic improvisation. It has been concluded that private hospitals’ use of strategic knowledge contributes to increasing strategic improvisation

Enhancing knowledge mastery in resident students through peer-teaching: a study in respiratory medicine

AimThe transition from medical students to competent physicians requires comprehensive training during residency programs. In China, resident students typically undergo 2- or 3-year training programs. While they learn from patient interactions under the guidance of experienced doctors, integrating theoretical knowledge from textbooks into practical cases remains a challenge. This study aimed to explore the impact of medical interns acting as peer-students on the knowledge mastery of resident students.MethodThe participants of this study consisted of resident students specializing in respiratory medicine at the Second Affiliated Hospital of Zhejiang University, School of Medicine. Resident students were given the opportunity to volunteer as peer-teachers for medical interns in the respiratory department. Those who chose to instruct interns were automatically placed into the test group, while those who opted not to partake in intern instruction formed the control group. In their role as peer-teachers, resident students assumed the responsibility of guiding interns in patient management throughout the entire continuum, spanning from initial engagement to discharge, a commitment that extended over a minimum period of 2 weeks. The resident students' academic performance was evaluated through a departmental examination consisting of 50 multiple-choice questions, which was administered upon completing their rotation. Statistical analysis was performed to assess the impact of peer-teaching on the resident students’ performance.ResultsBetween January 2023 and June 2023, a total of 158 resident students completed their rotation in the respiratory department. Among them, 40 resident students willingly took on the responsibility of instructing medical interns, while 118 resident students did not participate in intern teaching. With a "one-to-one" teaching policy in place, the overall satisfaction rate of the interns was an impressive 95.35%. Pre-rotation test scores for the test group averaged 81.66 ± 8.325 (Mean ± SD) and the control group averaged 81.66 ± 8.002, without significance. The departmental examination scores of the test group averaged 85.60 ± 7.886, while the control group scored an average of 82.25 ± 8.292, with a statistically significant difference (p = 0.027).ConclusionIn conclusion, our study underscores the positive influence of peer-teaching on the knowledge mastery of resident students.

Les agressions sexuelles sur mineurs : bilan d’activité de l’unité de médecine légale du CHU Ibn Sina de Rabat en 2022

Introduction: Child sexual abuse is a public health problem. The challenge for the forensic doctor is to correctly interpret the forensic examination findings.Objective: To study a population of victims of child sexual abuse who consulted at the forensic medicine unit of Ibn Sina Hospital in Rabat in 2022.Material and methods: Were collected demographic data, aggression modalities and victims’ examination findings.Results: Out of 125 children, there were 92 girls and 33 boys. The median age was 14 [8;16] years old. Normal genital examinations concerned 23 (36.4%) victims who had undergone vaginal penetration. When abnormal, genital examination showed recent hymeneal tearing in 5% of cases (n = 3) and non-specific signs in the rest of the cases. Normal anal examination concerned 51.2% (n = 42) of the victims who had undergone anal penetration. When abnormal, anal examinations showed a recent anal fissure in three victims (6.97%) and non-specific signs in the rest of the cases. Clinical examinations were considered compatible with the reported facts for 32 victims (25.6%).Conclusion: In child sexual abuse, the examination is frequently normal. The lesions, if present, must be interpreted taking into account several factors (inter alia, consultation time) and, at best, by an experienced forensic doctor.

External quality assurance program for diagnostic complement laboratories: evaluation of the results of the past seven years.

The complement external quality assurance (EQA) program was first organized in 2010 by a group of researchers working in diagnostic complement laboratories. Starting in 2016, INSTAND e.V., a German, non-profit interdisciplinary scientific medical society dedicated to providing expert EQA programs for medical laboratories, started organizing the EQAs for complement diagnostic laboratories together with the same group of experienced scientists and doctors who also work as EQA experts. The aim of the current work is to provide descriptive analysis of the past seven years' complement EQA results and evaluate timeline changes in proficiency testing. Each year, in March and October, blinded samples (normal, pathological) were sent to the participating diagnostic laboratories, where complement parameters were evaluated exactly as in daily routine samples. Since no reference method/target values exist for these parameters, and participants used different units for measurement, the reported results were compared to the stable mean (Algorithm A) of the participants using the same method/measurement units. A reported result was qualified as "passed" if it fell into the 30-50% evaluation/target range around the mean of reported results (depending on the given parameter). While the number of participating laboratories has increased in the past years (from around 120 to 347), the number of complement laboratories providing multiple determinations remained mostly unchanged (around 30 worldwide). C3, C4, C1-inhibitor antigen and activity determinations provided the best proficiency results, with >90% passing quotas in the past years, independent of the applied method. Determination of the functional activity of the three activation pathways was good in general, but results showed large variance, especially with the pathological samples. Complement factor C1q and regulators FH and FI are determined by only a few laboratories, with variable outcomes (in general in the 85-90% pass range). Activation products sC5b-9 and Bb were determined in 30 and 10 laboratories, respectively, with typical passing quotas in the 70-90% range, without a clear tendency over the past years. With these accumulated data from the past seven years, it is now possible to assess sample-, method-, and evaluation related aspects tofurtherimprove proficiency testing and protocolize diagnostic complement determinations.

Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images.

Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we developed and tested a deep-learning algorithm to detect pulmonary arterial hypertension using chest X-ray (CXR) images. From the image archive of Chiba University Hospital, 259 CXR images from 145 patients with pulmonary arterial hypertension and 260 CXR images from 260 control patients were identified; of which 418 were used for training and 101 were used for testing. Using the testing dataset for each image, the algorithm outputted a numerical value from 0 to 1 (the probability of the pulmonary arterial hypertension score). The training process employed a binary cross-entropy loss function with stochastic gradient descent optimization (learning rate parameter, α = 0.01). In addition, using the same testing dataset, the algorithm's ability to identify pulmonary arterial hypertension was compared with that of experienced doctors. The area under the curve (AUC) of the receiver operating characteristic curve for the detection ability of the algorithm was 0.988. Using an AUC threshold of 0.69, the sensitivity and specificity of the algorithm were 0.933 and 0.982, respectively. The AUC of the algorithm's detection ability was superior to that of the doctors. The CXR image-derived deep-learning algorithm had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors.

Chinese guildeline on diagnosis and treatment of basal cell carcinoma (2023 edition)

Basal cell carcinoma (BCC) is the most common skin malignancy, with a higher prevalence in Caucasians than in East Asians. Although there is a lack of epidemiological data in China, it is generally believed that the incidence of BCC in China is increasing due to the aging population. A variety of risk factors are related to the occurrence of BCC, among which ultraviolet rays and gene mutations play a major role, especially the abnormal activation of Hedgehog (Hh) signaling pathway, which is considered to be the most important pathogenesis of BCC. The clinical manifestations of BCC are highly specific, and most experienced doctors can make a preliminary diagnosis by clinical manifestations. Dermoscopy and other imaging methods can greatly improve the accuracy of diagnosis, but there are still some atypical or rare types of BCC that need further confirmation through histopathological examination. This guideline is initiated by the National Clinical Research Center for Skin and Immune Diseases (based on Peking University First Hospital). It has invited a panel of experts consisting of 24 senior dermatologists specializing in dermatologic surgery from the Dermatologic Surgery Group of the Chinese Medical Doctor Association of Dermatology, the Dermatologic Surgery Group of the Dermatology & Venereology Committee, Chinese Association of Integration Medicine, and the Dermatologic Surgery and Cosmetic Branch of Clina Leprosy Association. In addition, experts from the Burn and Plastic Surgery (Maxillofacial), Ophthalmology, Otolaryngology, Head and Neck Surgery, Radiation Therapy, and Pathology were also invited to participate. This panel forms the "Chinese Guideline for the Diagnosis and Treatment of Basal Cell Carcinoma" expert group. Based on the latest domestic and international research findings, the guideline was developed through four rounds of discussions by the expert group and revised to provide valuable references for clinical healthcare providers in the diagnosis and treatment of BCC.

The Value of Trichoscopy in the Follow-up of Treatment Response in Patients With Androgenetic Alopecia.

Although trichoscopy has been used successfully in the diagnosis of androgenetic alopecia (AGA), its utility in monitoring the therapeutic outcome is not clear. To investigate the value of trichoscopy in the follow-up of treatment response in patients with AGA. Ninety-five patients with AGA were included. Trichoscopic images were obtained on a 1 cm2 circular area in the left frontoparietal scalp pre- and post-treatment in all and in 61 patients, respectively. Two doctors evaluated those images who are less experienced (doctor A) and experienced (doctor B) in hair diseases and trichoscopy. Terminal, vellus and total hair count (THC, VHC, ToHC) and terminal to vellus hair (T/V) ratio were determined. These numerical data were also calculated by TrichoScan analysis of the same area. The agreement between trichoscopy and TrichoScan data was assessed. The agreement between doctor A and TrichoScan was moderate for THC and ToHC pre-treatment and good post-treatment; poor for VHC and T/V ratio pre- and post-treatment. The agreement between doctor B and TrichoScan was excellent for THC, VHC and ToHC; good for T/V ratio pre- and post-treatment. Trichoscopy, when performed by a doctor experienced in hair diseases and trichoscopy, can be used as a sensitive method in monitoring the treatment response in patients with AGA which is based on the determination of THC, VHC, ToHC and T/V ratio. If it is to be performed by a less experienced doctor, it can be preferred as a sensitive follow-up method using THC and ToHC.

BPSegSys: A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning

Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthesia method that can be used to observe the target nerve and its surrounding structures, the puncture needle's advancement and local anesthetics spread in real time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently. We established a public data set containing 320 ultrasound images of brachial plexus (BP). Three experienced doctors jointly produced the BP segmentation ground truth and labeled brachial plexus trunks. We designed a brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys achieves experienced-doctor-level nerve identification performance in various experiments. We evaluated BPSegSys performance in terms of intersection-over-union (IoU). Considering three data set groups in our established public data set, the IoUs of BPSegSys were 0.5350, 0.4763 and 0.5043, respectively, which exceed the IoUs 0.5205, 0.4704 and 0.4979 of experienced doctors. In addition, we determined that BPSegSys can help doctors identify brachial plexus trunks more accurately, with IoU improvement up to 27%, which has significant clinical application value. We establish a data set for brachial plexus trunk identification and designed a BPSegSys to identify the brachial plexus trunks. BPSegSys achieves the doctor-level identification of the brachial plexus trunks and improves the accuracy and efficiency of doctors' identification of the brachial plexus trunks.

Assessing the utility of artificial intelligence throughout the triage outpatients: a prospective randomized controlled clinical study.

Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natural language processing tool powered by artificial intelligence technology, is increasingly utilized in medicine. To facilitate and expedite patients' navigation to the appropriate department, we conducted an outpatient triage evaluation of ChatGPT. For this evaluation, we posed 30 highly representative and common outpatient questions to ChatGPT and scored its responses using a panel of five experienced doctors. The consistency of manual triage and ChatGPT triage was assessed by five experienced doctors, and statistical analysis was performed using the Chi-square test. The expert ratings of ChatGPT's answers to these 30 frequently asked questions revealed 17 responses earning very high scores (10 and 9.5 points), 7 earning high scores (9 points), and 6 receiving low scores (8 and 7 points). Additionally, we conducted a prospective cohort study in which 45 patients completed forms detailing gender, age, and symptoms. Triage was then performed by outpatient triage staff and ChatGPT. Among the 45 patients, we found a high level of agreement between manual triage and ChatGPT triage (consistency: 93.3-100%, p<0.0001). We were pleasantly surprised to observe that ChatGPT's responses were highly professional, comprehensive, and humanized. This innovation can help patients win more treatment time, improve patient diagnosis and cure rates, and alleviate the pressure of medical staff shortage.

Impact of Ultra-widefield Imaging on Understanding the Pathophysiology of Peripheral Retinal Degeneration

Ultra-widefield imaging has revolutionized the understanding of the retinal periphery in clinical practice. Since the publication of the study “Comparison of image-assisted versus traditional fundus examination,” the practice of using fundus imaging to guide retinal examination has become ubiquitous, not only by experienced doctors in practice, but in the training of new clinicians.1 Quick capture and review of micron-level views with various wavelengths allows clinicians to plan their fundus exam, focus their time on areas of suspicion and better understand the health of the entire retina more effectively and efficiently. Image-assisted ophthalmoscopy has excellent agreement for a variety of lesion types but confers an advantage in optic nerve assessment, review of small lesions in the mid and far periphery and in visualization of vitreous lesions. Increasing resolution, integration of new wavelengths, auto-montage, optical coherence tomography and artificial intelligence have improved the sensitivity of this tool - thus improving the overall efficiency of a fundus exam, improving outcomes and leading to more accurate assessment of risk. Advances in ultra-widefield imaging technology have allowed us to better understand the anatomy and physiology of the peripheral retina and its relation to sight threatening disease; making this an invaluable tool to educate our students in training as well as our patients with and without retinal pathology in the clinic. When an image or lesion of concern can be viewed simultaneously by student and instructor or by doctor and patient, the learning and understanding from this ultra-widefield perspective are invaluable. In this review, we explore the technological advances and the impact on detection, diagnosis and prognostic potential in a variety of retinal degenerations. In addition, we examine remaining challenges of the technology.