Non-Generative Artificial Intelligence (AI) in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning.

The use of Artificial Intelligence (AI) within pathology and healthcare has advanced extensively. We have accordingly witnessed increased adoption of various AI tools which are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within healthcare thus far fall mostly under the non-generative AI domain, which includes supervised and unsupervised machine learning (ML) techniques.

Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning.

Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reoperation will allow for improved shared surgical decision making and patient-specific and preoperative optimisation. Yet, no precise machine learning models have been published to perform well in predicting the need for reoperation within 30 days following primary total shoulder arthroplasty (TSA).

Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.

Objectives: Recently, deep learning medical image analysis in orthopedics has become highly active. However, progress has been restricted by the absence of large-scale and standardized ground-truth images. To the best of our knowledge, this study is the first to propose an innovative solution, namely a deep few-shot image augmentation pipeline, that addresses this challenge by synthetically generating knee radiographs for training downstream tasks, with a specific focus on knee osteoarthritis Kellgren-Lawrence (KL) grading.

Development and Validation of a Mobile Phone Application for Measuring Knee Range of Motion.

Knee range of motion (ROM) is an important indicator of knee function. Outside the clinical setting, patients may not be able to accurately assess knee ROM, which may impair recovery following trauma or surgery. This study aims to validate a smartphone mobile application developed to measure knee ROM compared to visual and goniometer ROM measurements.

HexAI-TJAtxt: A textual dataset to advance open scientific research in total joint arthroplasty.

Total joint arthroplasty (TJA) is the most common and fastest inpatient surgical procedure in the elderly, nationwide. Due to the increasing number of TJA patients and advancements in healthcare, there is a growing number of scientific articles being published in a daily basis. These articles offer important insights into TJA, covering aspects like diagnosis, prevention, treatment strategies, and epidemiological factors.

Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization.

Introduction: The efficacy of cardiac resynchronization therapy (CRT) has been widely studied in the medical literature; however, about 30% of candidates fail to respond to this treatment strategy. Smart computational approaches based on clinical data can help expose hidden patterns useful for identifying CRT responders.

Methods: We retrospectively analyzed the electronic health records of 1664 patients who underwent CRT procedures from January 1, 2002 to December 31, 2017.

Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis.

. Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency.

Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Knee Arthroplasty.

Background: Natural language processing (NLP) methods have the capability to process clinical free text in electronic health records, decreasing the need for costly manual chart review, and improving data quality. We developed rule-based NLP algorithms to automatically extract surgery specific data elements from knee arthroplasty operative notes.

Methods: Within a cohort of 20,000 knee arthroplasty operative notes from 2000 to 2017 at a large tertiary institution, we randomly selected independent pairs of training and test sets to develop and evaluate NLP algorithms to detect five major data elements.

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs.

Relationship Between Very Cold Outside Weather and Surgical Outcome: Integrating Shallow and Deep Artificial Neural Nets.

Patients' hospital length of stay (LOS) as a surgical outcome is important indicator of quality of care. We used EMR data to build artificial neural network models to better understand the impact of cold weather on outcome of first surgeries in a day in comparison to a matched cohort receiving surgical treatment in warm days. We found that LOS for first-in-a-day cardiac and orthopedic surgical cases are longer in very cold days.

MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection.

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. The current dataset entitled includes more than 20,000 digital images from three different indoor object categories, including doors, stairs, and hospital signs.

Quantifying predictive capability of electronic health records for the most harmful breast cancer.

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models for the "most harmful" breast cancers have rarely been developed. Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHR variables in the "most harmful" breast cancer risk prediction.

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.

Background: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs.

Objective: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs.

SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples.

Scanning electron microscopy (SEM) imaging has been a principal component of many studies in biomedical, mechanical, and materials sciences since its emergence. Despite the high resolution of captured images, they remain two-dimensional (2D). In this work, a novel framework using sparse-dense correspondence is introduced and investigated for 3D reconstruction of stereo SEM images.

SparkText: Biomedical Text Mining on Big Data Framework.

Background: Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment.

Results: In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database.

3DSEM++: Adaptive and intelligent 3D SEM surface reconstruction.

Structural analysis of microscopic objects is a longstanding topic in several scientific disciplines, such as biological, mechanical, and materials sciences. The scanning electron microscope (SEM), as a promising imaging equipment has been around for decades to determine the surface properties (e.g.

3DSEM: A 3D microscopy dataset.

The Scanning Electron Microscope (SEM) as a 2D imaging instrument has been widely used in many scientific disciplines including biological, mechanical, and materials sciences to determine the surface attributes of microscopic objects. However the SEM micrographs still remain 2D images. To effectively measure and visualize the surface properties, we need to truly restore the 3D shape model from 2D SEM images.

Recent advances in 3D SEM surface reconstruction.

The scanning electron microscope (SEM), as one of the most commonly used instruments in biology and material sciences, employs electrons instead of light to determine the surface properties of specimens. However, the SEM micrographs still remain 2D images. To effectively measure and visualize the surface attributes, we need to restore the 3D shape model from the SEM images.