Could Artificial Intelligence Prevent Intraoperative Anaphylaxis? Reference Review and Proof of Concept

Artificial intelligence and machine learning for anaphylaxis algorithms

PURPOSE OF REVIEW

Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management.

RECENT FINDINGS

Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed.

SUMMARY

Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.

Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence

This policy analysis focuses on harnessing the power of artificial intelligence (AI) in hospital quality improvement to transform quality and patient safety. It examines the application of AI at the two following fundamental levels: (1) diagnostic and treatment and (2) clinical operations. AI applications in diagnostics directly impact patient care and safety. At the same time, AI indirectly influences patient safety at the clinical operations level by streamlining (1) operational efficiency, (2) risk assessment, (3) predictive analytics, (4) quality indicators reporting, and (5) staff training and education. The challenges and future perspectives of AI application in healthcare, encompassing technological, ethical, and other considerations, are also critically analyzed.

Applications and challenges of neural networks in otolaryngology (Review)

Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.

A Randomized Comparison of Multimodal Analgesia and Fentanyl-Based Patient-Controlled Analgesia in Women Undergoing Robot-Assisted Bilateral Axillary Breast Approach Thyroidectomy

Opioid-free multimodal analgesia (MMA) emerges as a preferable approach for postoperative pain management compared to opioid-based patient-controlled analgesia (PCA) in robot-assisted bilateral axillary breast approach thyroidectomy, a procedure commonly undergone by young female patients. We compared the analgesic efficacy and other recovery profiles between MMA and PCA. In total, 88 female patients were administered fentanyl-based PCA or the combination of lidocaine continuous infusion and nefopam injection before recovery from general anesthesia. The visual analog scale score of postoperative pain was assessed at the post-anesthesia care unit and at 6, 12, and 24 h after the termination of surgery. Postoperative nausea and vomiting (PONV), rescue analgesic and anti-emetic agents, recovery profiles, and adverse events were also compared. The median numeric rating scores on postoperative pain at 6 h after recovery from general anesthesia were three in both groups, with no significant difference between the groups at any time point. The PONV incidence was significantly higher in the PCA group than in the MMA group. The combination of systemic lidocaine infusion and nefopam injection has an analgesic effect equivalent to that of fentanyl-based PCA without PONV.

Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches

BACKGROUND AND OBJECTIVES

Literature reviews are foundational to understanding medical evidence. With AI tools like ChatGPT, Bing Chat and Bard AI emerging as potential aids in this domain, this study aimed to individually assess their citation accuracy within Nephrology, comparing their performance in providing precise.

MATERIALS AND METHODS

We generated the prompt to solicit 20 references in Vancouver style in each 12 Nephrology topics, using ChatGPT, Bing Chat and Bard. We verified the existence and accuracy of the provided references using PubMed, Google Scholar, and Web of Science. We categorized the validity of the references from the AI chatbot into (1) incomplete, (2) fabricated, (3) inaccurate, and (4) accurate.

RESULTS

A total of 199 (83%), 158 (66%) and 112 (47%) unique references were provided from ChatGPT, Bing Chat and Bard, respectively. ChatGPT provided 76 (38%) accurate, 82 (41%) inaccurate, 32 (16%) fabricated and 9 (5%) incomplete references. Bing Chat provided 47 (30%) accurate, 77 (49%) inaccurate, 21 (13%) fabricated and 13 (8%) incomplete references. In contrast, Bard provided 3 (3%) accurate, 26 (23%) inaccurate, 71 (63%) fabricated and 12 (11%) incomplete references. The most common error type across platforms was incorrect DOIs.

CONCLUSIONS

In the field of medicine, the necessity for faultless adherence to research integrity is highlighted, asserting that even small errors cannot be tolerated. The outcomes of this investigation draw attention to inconsistent citation accuracy across the different AI tools evaluated. Despite some promising results, the discrepancies identified call for a cautious and rigorous vetting of AI-sourced references in medicine. Such chatbots, before becoming standard tools, need substantial refinements to assure unwavering precision in their outputs.

Recent Advances and Adaptive Strategies in Image Guidance for Cervical Cancer Radiotherapy

The standard of care for locally advanced cervical cancer is external beam radiotherapy (EBRT) with simultaneous chemotherapy followed by an internal radiation boost. New imaging methods such as positron-emission tomography and magnetic resonance imaging have been implemented into daily practice for better tumor delineation in radiotherapy planning. The method of delivering radiation has changed with technical advances in qualitative imaging and treatment delivery. Image-guided radiotherapy (IGRT) plays an important role in minimizing treatment toxicity of pelvic radiation and provides a superior conformality for sparing the organs at risk (OARs) such as bone marrow, bowel, rectum, and bladder. Similarly, three-dimensional image-guided adaptive brachytherapy (3D-IGABT) with computed tomography (CT) or magnetic resonance imaging (MRI) has been reported to improve target coverage and reduce the dose to normal tissues. Brachytherapy is a complementary part of radiotherapy treatment for cervical cancer and, over the past 20 years, 3D-image-based brachytherapy has rapidly evolved and established itself as the gold standard. With new techniques and adaptive treatment in cervical cancer, the concept of personalized medicine is introduced with an enhanced comprehension of the therapeutic index not only in terms of volume (three-dimensional) but during treatment too (four-dimensional). Current data show promising results with integrated IGRT and IGABT in clinical practice and, therefore, better local control and overall survival while reducing treatment-related morbidity. This review gives an overview of the substantial impact that occurred in the progress of image-guided adaptive external beam radiotherapy and brachytherapy.

Computer-Assisted Clinical Diagnosis and Treatment

PURPOSE OF REVIEW

Computer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes.

RECENT FINDINGS

CAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.

Gallbladder Pancreatic Heterotopia-The Importance of Diagnostic Imaging in Managing Intraoperative Findings

Pancreatic heterotopy is a rare entity defined as the presence of abnormally located pancreatic tissue without any anatomical or vascular connection to the normal pancreas. Heterotopic pancreatic tissue can be found in various regions of the digestive system, such as the stomach, duodenum, and upper jejunum, with the less commonly reported location being the gallbladder. Gallbladder pancreatic heterotopia can be either an incidental finding or diagnosed in association with cholecystitis. Pancreatitis of the ectopic tissue has also been described. In this context, we report three cases of heterotopic pancreatic tissue in the gallbladder with different types of pancreatic tissue according to the Heinrich classification. One patient was a 24-year-old male who presented with acute pancreatitis symptoms and an ultrasonographical detected mass in the gallbladder, which proved to be heterotopic pancreatic tissue. The other two cases were female patients aged 24 and 32, respectively, incidentally diagnosed on histopathological examination after cholecystectomy for symptomatic cholelithiasis. Both cases displayed chronic cholecystitis lesions; one of them was also associated with low grade dysplasia of the gallbladder. Although a rare occurrence in general, pancreatic heterotopia should be acknowledged as a possible incidental finding in asymptomatic patients as well as a cause for acute cholecystitis or pancreatitis.

Extracranial Facial Nerve Schwannoma-Histological Surprise or Therapeutic Planning?

Schwannomas (neurilemomas) are benign, slow-growing, encapsulated, white, yellow, or pink tumors originating in Schwann cells in the sheaths of cranial nerves or myelinated peripheral nerves. Facial nerve schwannomas (FNS) can form anywhere along the course of the nerve, from the pontocerebellar angle to the terminal branches of the facial nerve. In this article, we propose a review of the specialized literature regarding the diagnostic and therapeutic management of schwannomas of the extracranial segment of the facial nerve, also presenting our experience in this type of rare neurogenic tumor. The clinical exam reveals pretragial swelling or retromandibular swelling, the extrinsic compression of the lateral oropharyngeal wall like a parapharyngeal tumor. The function of the facial nerve is generally preserved due to the eccentric growth of the tumor pushing on the nerve fibers, and the incidence of peripheral facial paralysis in FNSs is described in 20-27% of cases. Magnetic Resonance Imaging (MRI) examination is the gold standard and describes a mass with iso signal to muscle on T1 and hyper signal to muscle on T2 and a characteristic "darts sign." The most practical differential diagnoses are pleomorphic adenoma of the parotid gland and glossopharyngeal schwannoma. The surgical approach to FNSs requires an experienced surgeon, and radical ablation by extracapsular dissection with preservation of the facial nerve is the gold standard for the cure. The patient's informed consent is important regarding the diagnosis of schwannoma and the possibility of facial nerve resection with reconstruction. Frozen section intraoperative examination is necessary to rule out malignancy or when sectioning of the facial nerve fibers is necessary. Alternative therapeutic strategies are imaging monitoring or stereotactic radiosurgery. The main factors which are considered during the management are the extension of the tumor, the presence or not of facial palsy, the experience of the surgeon, and the patient's options.

Complete Intradural Interbody Cage Migration in Lumbar Spine Surgery: A Case Report and Literature Review

Background: Spinal fusion is a common surgery, in which vertebrae are fused to restore spinal stability and eliminate pain during movement. The use of an interbody cage facilitates spinal fusion. However, complete cage migration into the dura matter rarely occurs and can be challenging to manage. Case Presentation: A 44-year-old man presented at our spine center with a history of incomplete paraplegia and cauda equina syndrome that had lasted for 2 years and 4 months. This condition developed after he underwent six lumbar spine surgeries to address lower back pain and right-sided sciatica. A structural allograft kidney-shaped cage was found completely within the dura at the level of the L3 vertebra. Durotomy, cage retrieval, and pedicle screw fixation from the L2 to L4 vertebrae were performed. Numbness in both lower limbs markedly decreased within several days of the operation. After four months following the progressive physical therapy, the patient could partially control both urination and defecation. Five months postoperatively, he could stand with slight assistance. Conclusions: Complete intradural cage migration is a rare and serious complication. To the best of our knowledge, this is the first reported case with such a condition in the literature. Even if treatment is delayed, surgical intervention may salvage the remaining neurologic function and may even lead to partial recovery.

Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities

Background and Objectives: Foot deformities are the basis of numerous disorders of the locomotor system. An optimized method of classification of foot deformities would enable an objective identification of the type of deformity since the current assessment methods do not show an optimal level of objectivity and reliability. The acquired results would enable an individual approach to the treatment of patients with foot deformities. Thus, the goal of this research study was the development of a new, objective model for recognizing and classifying foot deformities with the application of machine learning, by labeling baropodometric analysis data using computer vision methods. Materials and Methods: In this work, data from 91 students of the Faculty of Medicine and the Faculty of Sports and Physical Education, University of Novi Sad were used. Measurements were determined by using a baropodometric platform, and the labelling process was carried out in the Python programming language, using functions from the OpenCV library. Segmentation techniques, geometric transformations, contour detection and morphological image processing were performed on the images, in order to calculate the arch index, a parameter that gives information about the type of the foot deformity. Discussion: The foot over which the entire labeling method was applied had an arch index value of 0.27, which indicates the accuracy of the method and is in accordance with the literature. On the other hand, the method presented in our study needs further improvement and optimization, since the results of the segmentation techniques can vary when the images are not consistent. Conclusions: The labeling method presented in this work provides the basis for further optimization and development of a foot deformity classification system.