Is this an adenocarcinoma? A case report of extragenital endometriosis with hydroureteronephrosis.

Introduction And Importance: Endometriosis is a prevalent condition within the female reproductive age group, but its presentation and diagnosis still pose a challenge as it mimics other diseases and affects multiple organ systems.

Case Presentation: A 24-year-old nulliparous female presented with complaints of menorrhagia, lower abdominal pain, post-coital bleeding, and significant weight loss for 7 months.

Clinical Discussion: The case highlights the challenge of diagnosing endometriosis due to its ability to mimic other conditions, such as carcinoma cervix and rectum.

Prevalence of pre-operative anxiety and associated risk factors among patients awaiting elective surgery in a tertiary care hospital.

Background: Patients undergoing surgery have a fear of anesthesia and surgical procedures that results in anxiety. The global incidence of pre-operative anxiety is estimated at 60-92%. Age, gender, education, marital status, type of family, type of anesthesia and surgery, and history of surgery are the contributing factors.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval.

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient.

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

Background: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction.

Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease.

Background: Pain evaluation remains largely subjective in neurosurgical practice, but machine learning provides the potential for objective pain assessment tools.

Objective: To predict daily pain levels using speech recordings from personal smartphones of a cohort of patients with diagnosed neurological spine disease.

Methods: Patients with spine disease were enrolled through a general neurosurgical clinic with approval from the institutional ethics committee.

A systematic review of federated learning applications for biomedical data.

Objectives: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology.

Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice.

Accurate brain meningioma segmentation and volumetric assessment are critical for serial patient follow-up, surgical planning and monitoring response to treatment. Current gold standard of manual labeling is a time-consuming process, subject to inter-user variability. Fully-automated algorithms for meningioma segmentation have the potential to bring volumetric analysis into clinical and research workflows by increasing accuracy and efficiency, reducing inter-user variability and saving time.

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World.

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern.

Deep Learning for Adjacent Segment Disease at Preoperative MRI for Cervical Radiculopathy.

Background Patients who undergo surgery for cervical radiculopathy are at risk for developing adjacent segment disease (ASD). Identifying patients who will develop ASD remains challenging for clinicians. Purpose To develop and validate a deep learning algorithm capable of predicting ASD by using only preoperative cervical MRI in patients undergoing single-level anterior cervical diskectomy and fusion (ACDF).

Deep Tone Mapping Operator for High Dynamic Range Images.

A computationally fast tone mapping operator (TMO) that can quickly adapt to a wide spectrum of high dynamic range (HDR) content is quintessential for visualization on varied low dynamic range (LDR) output devices such as movie screens or standard displays. Existing TMOs can successfully tone-map only a limited number of HDR content and require an extensive parameter tuning to yield the best subjective-quality tone-mapped output. In this paper, we address this problem by proposing a fast, parameter-free and scene-adaptable deep tone mapping operator (DeepTMO) that yields a high-resolution and high-subjective quality tone mapped output.