Expert Physicians Research Articles

Management of Adult Patients with Newly Diagnosed or Relapsed Primary Immune Thrombocytopenia in Eastern Austria.

Treatment sequence in primary immune thrombocytopenia (ITP) is based on national and international recommendations, treatment availability, and physician expertise. This article aimed to provide real-world data on treatment sequence and responses to first- and second-line treatments in newly diagnosed and relapsed adult ITP patients. We analyzed a cohort of 46 adult ITP patients from the Vienna ITP Biobank, who started first-line therapy within 1 week before their first study visit between February 2016 and March 2023. We investigated clinical patient characteristics and patient management in our specialized center and examined the impact of the international ASH guidelines on ITP treatment. Forty-six primary ITP patients, 27 (58.7%) with newly diagnosed ITP and 19 (41.3%) with relapsed ITP, were investigated. Most patients were female (65.2%) with a median platelet count of 9 × 109/L, and 31 patients (67.4%) had bleeding symptoms. All patients received first-line treatment with oral prednisolone; 15 patients received oral prednisolone combined with intravenous immunoglobulins (IVIGs), which were more commonly administered in newly diagnosed than in relapsed ITP patients. First-line therapy resulted an overall response in 82.6% of patients after a median (interquartile range [IQR]) time of 10 (5-25) days. There was no difference in treatment responses between newly diagnosed and relapsed ITP patients, but newly diagnosed patients had a shorter time to response (median [IQR]: 8 [5-14] and 14 [8-27], p = 0.02). Twenty-three (50%) of the patients (11/27 newly diagnosed [40.7%], 12/19 relapsed [63.2%]) required second-line ITP therapy. Thrombopoietin-receptor agonists (TPO-RAs) were the most commonly used second-line therapy with a response rate of 73.7%, and a median (IQR) time to treatment response of 15 (12-20) days. Overall response rates to TPO-RA treatment did not differ between newly diagnosed and relapsed ITP. Following the publication of novel guidelines in 2019, the median (IQR) duration of corticosteroid treatment shortened (100-52 days, p = 0.01), as did the time to second-line treatment (160-47 days, p = 0.01), and the median number of first-line therapies decreased from 2 (1-3) to 1 (1-2). Initial treatment with corticosteroids was effective in the majority of newly diagnosed and relapsed ITP. Response rates to initial corticosteroid treatment in ITP patients are consistent with previous data, but only 50% achieve sustained remission. TPO-RAs, which are well tolerated and effective, are the most commonly used second-line therapy in our study population. International guidelines have led to faster treatment transitions and reduced splenectomy rates. Integration of real-life experience, expert consensus, and guidelines optimizes ITP patient management.

Effectiveness of King's Theory of Goal Attainment in Blood Glucose Management for Newly Diagnosed Patients With Type 2 Diabetes: Randomized Controlled Trial.

Diabetes poses a significant public health challenge in China and globally, with the number of patients expected to reach 592 million by 2035, notably in Asia. In China alone, an estimated 140 million individuals are living with diabetes, and a significant portion is nonadherent to medications, underscoring the urgency of effective management strategies. Recognizing the necessity of early and comprehensive management for newly diagnosed patients with type 2 diabetes, this study leverages an online teach-back method and "Internet + Nursing" platform based on King's Theory of Goal Attainment. The approach aims to enhance glycemic control and reduce fear and misconceptions about the disease, addressing both the educational and emotional needs of the patients. The primary aim of this study was to assess the effectiveness of King's Goal Attainment Theory in the management of newly diagnosed patients with type 2 diabetes. This research sought to develop a collaborative model for blood glucose management, integrating the expertise and roles of physicians, nurses, and patients. The model is designed to enhance the synergy in health care provision, ensuring a comprehensive approach to diabetes management. In this study conducted at Changzhou Traditional Chinese Medicine Hospital between January 2022 and February 2023, eligible patients were randomized into a control group or an online feedback group. The control group received standard care, while the online feedback group participated in a King's Theory of Goal Attainment-based online teach-back program, enhanced by "Internet + Nursing" strategies. This included an interactive platform for goal planning, video content sharing, comprehension assessment, misconception correction, and patient-driven recaps of disease information. Health monitoring was facilitated through the "Internet + Nursing" platform. The study focused on comparing changes in glucose metabolism and emotional disorder symptoms between the groups to evaluate the intervention's effectiveness. Following a 24-week intervention, we observed significant differences in key metrics between the online feedback group and the control group, each comprising 60 participants. The online feedback group demonstrated significant reductions in fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c (P<.05). Additionally, there was a notable decrease in hypoglycemia-related anxiety and alexithymia within this group. Conversely, the control group maintained relatively higher values for these metrics at the same time point (P<.05). These findings underscore the efficacy of online feedback in managing glycemic control and reducing psychological distress associated with hypoglycemia. The online teaching-back method, guided by King's Theory of Goal Attainment, effectively enhances glycemic control, reducing fasting plasma glucose, 2-hour postprandial glucose, and hemoglobin A1c levels in newly diagnosed patients with type 2 diabetes. Simultaneously, it alleviates hypoglycemia-related anxiety and mitigates alexithymia. This approach merits widespread promotion and implementation in clinical settings. Chinese Clinical Trial Registry ChiCTR2400079547; https://www.chictr.org.cn/showproj.html?proj=208223.

Consensus Statement: Postoperative Management after Balloon Dilation of the Eustachian Tube.

Balloon dilation of Eustachian tube (BDET) has been widely used as a minimally invasive treatment for obstructive Eustachian tube dysfunction (ETD). We used a Delphi consensus methodology to explore recommendations for clinical management of BDET in obstructive ETD. A Delphi panel of 26 expert physicians of otology participated in two rounds of anonymous, iterative questionnaires. Consensus is defined as ≥ 70% of panelists agree with recommendation and disagreement as < 70% agree. The responses from the Delphi study were analyzed using both the content validity ratio and Kendall's coefficient of concordance. The panel finally evaluated 26 topics, agreed 9 and did not reach consensus on 17 topics after 2 rounds. Although no consensus was reached on the postoperative follow-up period, a period of 12 months was most adopted. The Valsalva maneuver and questionnaire responses showed the highest agreement as postoperative assessment tools after BDET. Consensus was reached on some of the recommendations for the management of BEDT in obstructive ETD. The resultant agreement will provide directions for future research to describe standard postoperative management of BDET.

Empowering Healthcare: TinyML for Precise Lung Disease Classification

Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

The Digitized Memory Clinic.

Several major challenges, including an ageing population and declining workforce and the implementation of recent breakthrough therapies for Alzheimer disease, are prompting a necessary rethink of how people with neurodegenerative dementias are diagnosed and medically managed. Digital health technologies could play a pivotal part in this transformation, with new advances enabling the collection of millions of data points from a single individual. Possible applications include unobtrusive monitoring that aids early detection of disease and artificial intelligence-based health advice. To translate these advances to meaningful benefits for people living with a disease, technologies must be implemented within a system that retains the physician expert as a central figure in decision-making. This Perspective presents a new framework, termed the Digitized Memory Clinic, for the diagnostic pathway of neurodegenerative dementias that incorporates digital health technologies with currently available assessment tools, such as fluid and imaging biomarkers, in an interplay with the physician. The Digitized Memory Clinic will manage people across the entire disease spectrum, from the detection of risk factors for cognitive decline and the earliest symptoms to dementia, and will replace the present paradigm of a pure 'brick-and-mortar' memory clinic. Important ethical, legal and societal barriers associated with the implementation of digital health technologies in memory clinics need to be addressed. The envisioned Digitized Memory Clinic aims to improve diagnostics and enable precise disease-tracking prognostication for individuals with memory disorders and to open new possibilities, such as precision medicine for prevention and treatment.

A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.

Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics. We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models. Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign (P < 0.001) and cortical thickening (P < 0.001). In contrast, AI models were sensitive to abnormal gyration (P < 0.01) or gray-white matter blurring (P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs (P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) (P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17). This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.

Evaluation of a Physiologic-Driven Closed-Loop Resuscitation Algorithm in an Animal Model of Hemorrhagic Shock.

Appropriate resuscitation from hemorrhagic shock is critical to restore tissue perfusion and to avoid over-resuscitation. The objective of this study was to test the ability of a closed-loop diagnosis and resuscitation algorithm called resuscitation from shock using functional hemodynamic monitoring using invasive monitoring (ReFit1) and minimally invasive monitoring (ReFit2) to identify, treat, and stabilize a porcine model of severe hemorrhagic shock. We created a ReFit algorithm using dynamic hemodynamic parameters of pulse pressure variation (PPV), stroke volume variation (SVV), dynamic arterial elastance (Eadyn = PPV/SVV), driven by mean arterial pressure (MAP), mixed venous oxygen saturation, and heart rate targets to define the need for fluids, vasopressors, and inotropes. University-based animal laboratory. Twenty-seven female pigs. Anesthetized, intubated, and ventilated (8 mL/kg) pigs were bled at 10 mL/min until a MAP of less than 40 mm Hg, held for 30 minutes, then resuscitated. The ReFit algorithm used the above dynamic parameters to drive computer-controlled infusion pumps to deliver blood, lactated Ringer's solution, norepinephrine, and in ReFit1 dobutamine. In four animals, after initial resuscitation from hemorrhagic shock, the ability of the ReFit1 algorithm to treat acute air embolism-induced pulmonary hypertension and right heart failure was also tested. In 10 ReFit1 and 17 ReFit2 animals, the time to stabilization from shock was not dissimilar to open controlled resuscitation performed by an expert physician (52 ± 12, 50 ± 13, and 60 ± 15 min, respectively) with similar amounts of fluids and norepinephrine needed. In four ReFit1 animals after initial stabilization, the algorithm successfully resuscitated the animals after inducing an acute air embolism right heart failure, with all animals recovering stability within 30 minutes. Our physiologically based functional hemodynamic monitoring-centered closed-loop resuscitation system can effectively diagnose and treat cardiovascular shock due to hemorrhage and air embolism.

Malpractice litigation in multidisciplinary cancer care: Navigating medico-legal challenges and shared decision making in India

Healthcare professionals participating in multidisciplinary team (MDT) cancer meetings may not have a comprehensive understanding of their medicolegal responsibilities in current times. This article aims to delineate the principal medicolegal issues pertinent to multidisciplinary cancer care and offer recommendations for future implementation. Key concerns highlighted in this literature encompass patient consent and privacy during MDT meetings, professional liability, the formal expression of dissenting views and the duty of care. The analysis of existing literature prioritizes several recommendations for addressing these issues. Given the limited precedent available for formulating recommendations, this article identifies foundational evidence that can inform the practicing clinicians of these concerns in future MDT practices.Navigating decision-making processes in cancer care poses significant challenges for patients. This article seeks to elucidate the significance of shared decision making (SDM) within the Indian clinical context while pin pointing disparities compared to Western practices. Through a systematic search conducted in Medline and Google Scholar from 2000 to 2019, approximately 400 articles were screened, resulting in the selection of 43 relevant articles (5 from India and 38 from Western sources). The literature underscores a scarcity of information on shared decision making in India as compared to Western contexts, potentially leading to adverse physical, psychological and financial consequences reported by patients. According to one study, While Western data demonstrate extensive involvement of both patients and physicians in consensus-building for treatment decisions, such engagement in India is predominantly observed in tertiary care settings, academic institutions or cases with high therapy costs. Cultural beliefs and biases further influence patient engagement, while communication breakdowns correlate strongly with medicolegal malpractice litigations. Future research endeavours are warranted to explore strategies for integrating shared decision making into routine oncology practice in India. Physicians must actively involve patients or their immediate family members in decision-making processes to ensure a patient-centric approach, thereby mitigating the risks of un-informed decision-making or mistrust in the treating physician's expertise. Efforts such as physician and patient education, tool development, policy formulation, widespread implementation and periodic assessments hold promise for advancing the practice of shared decision making.

Physician perspectives about the diagnosis and management of acute chest syndrome.

Acute chest syndrome (ACS) is the leading cause of mortality, accounting for 25% of all deaths among individuals with sickle cell disease (SCD). There is a lack of evidence-based laboratory and clinical risk stratification guidelines for the diagnosis and management of ACS. To better understand physician practices for the management of ACS in the United States, we created an ACS Working Group including hematology and transfusion medicine physicians from four different SCD treatment centers in the United States. The working group created a physician survey that included physician demographics and ACS diagnostic criteria that they had to rate. The survey also included three case scenarios to assess physician attitudes about the management of ACS. Management options included supportive and preventive strategies in addition to transfusion therapy options. Out of 455 physicians who received the survey, 195 responded (response rate = 43%). The respondents were primarily hematology/oncology physicians. The responses showed wide variability among physicians in how diagnostic criteria for ACS are used and how physicians risk-stratify ACS patients in their practice. The responses also reflected variability in the use of transfusions for ACS. Based on our results, we conclude that ACS is diagnosed and managed inconsistently among expert physicians, especially in their transfusion practices due to a lack of consensus on risk stratification criteria. Our data suggest an urgent need for well-designed prospective studies to provide evidence-based guidelines and minimize management variability among physicians who care for individuals with SCD and ACS.

7870 Exploring The Role Of ChatGPT In Pediatric Endocrinology Education; Exam Preparation And Question Generation

Abstract Disclosure: J. Tarkoff: None. A.G. Martinez Sanchez: None. Large language models (LLMs) hold substantial promise for improving physician knowledge and expertise. Their role in medical education and the generation of diverse diagnoses can be crucial, potentially leading to positive effects on clinical outcomes. The New England Journal of Medicine pediatric case challenges have examined ChatGPT [1]. Yet, its performance in specialized areas like Pediatric Endocrinology remains unexplored. This study assesses the effectiveness of ChatGPT 4 in responding to questions from the Pediatric Endocrine Self-Assessment Program (PESAP). It also examines if the AI is suitable for creating an educational quiz for residents and fellows. Methods: ChatGPT 4 underwent testing with questions from the 2021-2022 version of PESAP, utilizing the prompt: “Can you assist from the perspective of a pediatric endocrinologist with the following patient case”. Responses were evaluated for initial correctness, and performance was analyzed across various competency categories corresponding to the 7 “umbrella sections” of the tool (Adrenal, Bone, Carbohydrate and Lipid Metabolism/Obesity, Growth, Pituitary, Reproductive System, and Thyroid). Subsequently, we personalized the model by incorporating the questions and detailed responses from the PESAP. This customization resulted in a model that was then utilized to create a 10-question proof-of-concept quiz for four board-certified pediatric endocrinologists. The quiz included a scoring system designed to measure the extent and depth of ChatGPT knowledge. Results: ChatGPT 4 accurately answered 52% of PESAP questions, demonstrating varying performance across specific categories, ranging from 30% (Adrenal) to 78% (Reproductive System). In 16 questions, ChatGPT 4 did not provide an initial answer, requiring a specific request for a response. For questions related to thyroid cancer, explicit prompts were necessary to instruct responses based on the American Thyroid Association 2015 guidelines. In the endocrinologist quiz, the average score was 80%, ranging from 60% to 100%. Discussion: Currently, using ChatGPT 4 as the final diagnostic tool, especially in pediatric endocrinology, should be approached with caution, based on our assessment. However, when incorporating distinct Pediatric Endocrinology case studies, ChatGPT 4 successfully generated valuable educational questions, reinforcing fundamental concepts in the field. We anticipate that as LLMs advance and receive direct medical training, the out-of-the-box diagnostic accuracy will improve, leading to a transformative impact on medical education through this technology.

Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study.

Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73% (286/392) for Gemini Advanced, 76.5% (300/392) for Gemini, and 68.6% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6% (124/392) for Gemini Advanced, 42.6% (167/392) for Gemini, and 31.4% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence-enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use.

The Value of Liver Biopsy and Histology in Liver Disease Diagnosis and Patient Care-a Pragmatic Prospective Clinical Practice Study.

We aimed to examine the correlation of pre-biopsy clinical diagnosis with hepatic histopathology. Liver biopsy provides histologic information and informs physicians about the underlying clinical disease. We hypothesized that expert physicians' pre-biopsy clinical diagnoses may obviate the need for histopathological diagnosis. Patients undergoing liver biopsy to investigate a liver diagnosis were prospectively identified. In the 80 patients included, an anonymous validated questionnaire inquiring about the most likely clinical diagnosis and liver disease stage was completed prospectively by hepatologists before biopsy performance. The most common pre-biopsy diagnoses were alcoholic liver disease (19 diagnoses), followed by non-alcoholic steatohepatitis and autoimmune hepatitis (18 each). Overall, the predicted histologic diagnosis was the same as the histologic diagnosis in 51/80 patients (64%), and thus a new liver disease diagnosis was made in 36% of patients. The diagnosis with the greatest pre-biopsy and post-biopsy diagnosis discrepancy was autoimmune hepatitis, with the correct diagnosis being predicted in 6/18 (33%) of patients (other diagnoses included the following: non-alcoholic steatohepatitis/non-alcoholic fatty liver disease (4), alcoholic liver disease (3), drug-induced liver injury (3), others (2)). For fibrosis staging, when grouped as no fibrosis (F0), fibrosis (F1-F3), or cirrhosis (F4), the fibrosis stage was correctly predicted in 68% of patients (54/80). Notably, 7 patients (9%) who were initially thought to have no or early-stage fibrosis had F4 fibrosis, and 6/80 (8%) patients who were considered to have a liver disease diagnosis before their biopsy had normal histology. Although hepatology experts often predict the correct underlying liver disease diagnosis, histopathological diagnoses different from expected are common.

A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.

Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are developed to grade the skills of the sonographer and not the examination. The purpose of this study is to establish a grading system with substantial agreement among coders, thereby enabling the development of an automated assessment tool for FAST examinations using artificial intelligence (AI). Five coders labeled a set of FAST clips. Each coder was responsible for a different subset of clips (10% of the clips were labeled in triplicate to evaluate intercoder reliability). The clips were labeled with a quality score from 1 (lowest quality) to 5 (highest quality). Clips of 3 or greater were considered passing. An AI training model was developed to score the quality of the FAST examination. The clips were split into a training set, a validation set, and a test set. The predicted scores were rounded to the nearest quality level to distinguish passing from failing clips. A total of 1,514 qualified clips (1,399 passing and 115 failing clips) were evaluated in the final data set. This final data set had a 94% agreement between pairs of coders on the pass/fail prediction, and the set had a Krippendorff α of 66%. The decision threshold can be tuned to achieve the desired tradeoff between precision and sensitivity. Without using the AI model, a reviewer would, on average, examine roughly 25 clips for every 1 failing clip identified. In contrast, using our model with a decision threshold of 0.015, a reviewer would examine roughly five clips for every one failing clip - a fivefold reduction in clips reviewed while still correctly identifying 85% of passing clips. Integration of AI holds significant promise in improving the accurate evaluation of FAST images while simultaneously alleviating the workload burden on expert physicians. Diagnostic Test/Criteria; Level II.

SMLS-YOLO: an extremely lightweight pathological myopia instance segmentation method.

Pathological myopia is a major cause of blindness among people under 50 years old and can result in severe vision loss in extreme cases. Currently, its detection primarily relies on manual methods, which are slow and heavily dependent on the expertise of physicians, making them impractical for large-scale screening. To tackle these challenges, we propose SMLS-YOLO, an instance segmentation method based on YOLOv8n-seg. Designed for efficiency in large-scale screenings, SMLS-YOLO employs an extremely lightweight model. First, StarNet is introduced as the backbone of SMLS-YOLO to extract image features. Subsequently, the StarBlock from StarNet is utilized to enhance the C2f, resulting in the creation of the C2f-Star feature extraction module. Furthermore, shared convolution and scale reduction strategies are employed to optimize the segmentation head for a more lightweight design. Lastly, the model incorporates the Multi-Head Self-Attention (MHSA) mechanism following the backbone to further refine the feature extraction process. Experimental results on the pathological myopia dataset demonstrate that SMLS-YOLO outperforms the baseline YOLOv8n-seg by reducing model parameters by 46.9%, increasing Box mAP@0.5 by 2.4%, and enhancing Mask mAP@0.5 by 4%. Furthermore, when compared to other advanced instance segmentation and semantic segmentation algorithms, SMLS-YOLO also maintains a leading position, suggesting that SMLS-YOLO has promising applications in the segmentation of pathological myopia images.

Top Ten Tips Palliative Care Clinicians Should Know About Caring for Patients Under Guardianships.

Palliative care clinicians often help facilitate coordination of care, complex serious illness, and end-of-life medical decision-making. However, the clinical and legal issues related to guardianship can complicate the decision-making process, care delivery, outcomes, and the role of the palliative care clinician. Adult patients who have a guardian have been found by a court to be unable to make some or all decisions for themselves. Providing care for patients under guardianship is where medicine overlaps with legal rights. It is crucial to be familiar with the patients' rights and the guardians' responsibilities to clarify medical decision-making processes and identify necessary authorities. This article uses an interprofessional approach to leverage the expertise of physicians, nurses, lawyers, and guardians and to guide palliative care clinicians to optimally support patients under guardianship.

Standardized Data Elements for Patients With Acute Pulmonary Embolism: A Consensus Report from the Pulmonary Embolism Research Collaborative.

Recent advances in therapy and the promulgation of multidisciplinary pulmonary embolism teams show great promise to improve management and outcomes of acute pulmonary embolism (PE). However, the absence of randomized evidence and lack of consensus leads to tremendous variations in treatment and compromises the wide implementation of new innovations. Moreover, the changing landscape of health care, where quality, cost, and accountability are increasingly relevant, dictates that a broad spectrum of outcomes of care must be routinely monitored to fully capture the impact of modern PE treatment. We set out to standardize data collection in patients with PE undergoing evaluation and treatment, and thus establish the foundation for an expanding evidence base that will address gaps in evidence and inform future care for acute PE. To do so, >100 international PE thought leaders convened in Washington, DC, in April 2022 to form the Pulmonary Embolism Research Collaborative. Participants included physician experts, key members of the US Food and Drug Administration, patient representatives, and industry leaders. Recognizing the multidisciplinary nature of PE care, the Pulmonary Embolism Research Collaborative was created with representative experts from stakeholder medical subspecialties, including cardiology, pulmonology, vascular medicine, critical care, hematology, cardiac surgery, emergency medicine, hospital medicine, and pharmacology. A list of critical evidence gaps was composed with a matching comprehensive set of standardized data elements; these data points will provide a foundation for productive research, knowledge enhancement, and advancement of clinical care within the field of acute PE, and contribute to answering urgent unmet needs in PE management. Evidence produced through the Pulmonary Embolism Research Collaborative, as it is applied to data collection, promises to provide crucial knowledge that will ultimately produce a robust evidence base that will lead to standardization and harmonization of PE management and improved outcomes.

Critical Steps for Determining Capacity to Refuse Emergency Medical Services Transport: A Modified Delphi Study

ABSTRACT OBJECTIVES Emergency physicians without specialized Emergency Medical Services (EMS) training are often required to provide online medical oversight. One common ethical question faced by these physicians is the assessment for decision-making capacity in a patient who does not accept EMS transport to the hospital. We sought expert consensus for a standardized set of guiding questions and recommendations to ensure a rigorous and feasible capacity assessment. METHODS A modified Delphi method approach was used to achieve group consensus among expert individuals. Nineteen physician experts were recruited from across the country, representing populations totaling over 22 million and a variety of urban, suburban, and rural practice environments. Experts completed a Round 1 survey that included 19 questions surrounding best practices for capacity evaluation among patients refusing transport. The threshold for consensus was predefined as 80% agreement. Participants gathered virtually meeting where the results from the first round were shared with the group. Discussion generated new items and refined the language of existing items. Following the virtual meeting, a Round 2 survey was conducted, and voted on by the panel for the items that did not meet consensus in Round 1. RESULTS After the first round, 15 of 19 items reached consensus. Three of the items that met consensus were universally noted to require language modification for clarification. A large portion of the discussion involved the proper method of integrating patient concerns around ambulance transport (e.g., cost of transport, financial concerns, social barriers) into the capacity assessment and whether alternate care options should be discussed. After the second round of voting, one additional item was reversed to meet consensus, resulting in a total of 16 items. CONCLUSIONS A consensus expert panel was able to agree upon 16 standardized steps to guide best practices and assist emergency physicians in real-time evaluation of patients that refuse EMS transport.

A deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes

BackgroundDetection of atrial tachyarrhythmias (ATA) on long-term ECG recordings is a pre-requisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep Learning (DL) algorithms provide improved performances on resting ECG databases. However, results on long-term Holter recordings are scarce. ObjectiveWe aimed to build and evaluate a DL modular software using ECG features well known to cardiologists with a user interface that allows cardiologists to adjudicate the results and drive a second DL analysis. MethodsUsing a large (n=187 recordings, 249,419 one-minute samples), beat-to-beat annotated, two-lead Holter database, we built a DL algorithm with a modular structure mimicking expert physician ECG interpretation to classify atrial rhythms. The DL network includes 3 modules (cardiac rhythm regularity, electrical atrial waveform, and raw voltage by time data) followed by a decision network and a long-term weighting factor . The algorithm was validated on an external database. ResultsF1 scores of our classifier were 99% for ATA detection, 95% for Atrial fibrillation and 90% for Atrial flutter respectively. Using the external MIT-database, the classifier obtains F1-score of 97% for the NSR class and 96% for the ATA class. Residual errors could be corrected by manual deactivation of one module in 7/15 of the recordings with an accuracy < 90%. ConclusionA DL modular software using ECG features well known to cardiologists provided an excellent overall performance. Clinically significant residual errors were most often related to the classification of the atrial arrhythmia type (fibrillation vs. flutter). The modular structure of the algorithm helped to edit and correct the AI-based first-pass analysis and will provide a basis for explainability.

Clinical performance of existing diagnostic criteria for pneumonia in older emergency patients: A prospective cohort study.

Pneumonia accounts for over half a million older adult emergency department (ED) visits annually, but ED pneumonia diagnosis is inaccurate. Geriatric-specific pneumonia diagnostic criteria exist for other settings; no prospective data exist to determine if application in the older adult ED population is feasible. The objective was to prospectively evaluate the utility of four current diagnostic criteria (Loeb; Modified McGeer; Infectious Disease Society of America/American Thoracic Society; American College of Emergency Physicians) in older adult ED patients. This was a prospective, observational cohort study of older adult ED patients ≥65 years of age in two U.S. EDs with suspected pneumonia defined as having chest radiography ordered and treating physician suspicion. The standard we used for defining the presence, absence, or inability to determine a diagnosis of pneumonia diagnosis was expert physician chart adjudication. We report the summary statistics for demographic characteristics and symptoms/exam findings and sensitivity, specificity, and likelihood ratios with 95% confidence intervals of the existing diagnostic criteria. Pre-specified cutoff values of a positive LR >10 and a negative LR <0.3 were considered clinically significant. Of 135 patients enrolled, 27 had pneumonia by adjudicator review. Typical patient-reported pneumonia symptoms, such as fever (18.5%) and new/worse cough (51.9%), were not consistently present in pneumonia. The IDSA/ATS and ACEP criteria had positive LR >10 and negative LR <0.3; however, all confidence intervals included pre-specified cutoffs. Older adults presented to the ED with low frequency of typical pneumonia symptoms. Although existing diagnostic definitions had promising test characteristics, they may not perform well enough for clinical application without refinement.

Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks.

In clinical practice, thyroid nodules are typically visually evaluated by expert physicians using 2D ultrasound images. Based on their assessment, a fine needle aspiration (FNA) may be recommended. However, visually classifying thyroid nodules from ultrasound images may lead to unnecessary fine needle aspirations for patients. The aim of this study is to develop an automatic thyroid ultrasound image classification system to prevent unnecessary FNAs. An automatic computer-aided artificial intelligence system is proposed for classifying thyroid nodules using a fine-tuned deep learning model based on the DenseNet architecture, which incorporates an attention module. The dataset comprises 591 thyroid nodule images categorized based on the Bethesda score. Thyroid nodules are classified as either requiring FNA or not. The challenges encountered in this task include managing variability in image quality, addressing the presence of artifacts in ultrasound image datasets, tackling class imbalance, and ensuring model interpretability. We employed techniques such as data augmentation, class weighting, and gradient-weighted class activation maps (Grad-CAM) to enhance model performance and provide insights into decision making. Our approach achieved excellent results with an average accuracy of 0.94, F1-score of 0.93, and sensitivity of 0.96. The use of Grad-CAM gives insights on the decision making and then reinforce the reliability of the binary classification for the end-user perspective. We propose a deep learning architecture that effectively classifies thyroid nodules as requiring FNA or not from ultrasound images. Despite challenges related to image variability, class imbalance, and interpretability, our method demonstrated a high classification accuracy with minimal false negatives, showing its potential to reduce unnecessary FNAs in clinical settings.