Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.

Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (maximum height, breadth, and thickness). Data was collected from 180 skeletons of a contemporary Italian population (83 males and 97 females) as well as from an independent sample of 21 forensic cases (13 males and 8 females).

Machine Learning Prediction Model to Predict Length of Stay of Patients Undergoing Hip or Knee Arthroplasties: Results from a High-Volume Single-Center Multivariate Analysis.

The growth of arthroplasty procedures requires innovative strategies to reduce inpatients' hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed.

Unveiling the female experience through adult mortality and survivorship in Milan over the last 2000 years.

This study challenges historical paradigms using a large-scale integrated bioarchaeological approach, focusing on the female experience over the last 2,000 years in Milan, Italy. Specifically, 492 skeletons from the osteological collection of Milan were used to elucidate female survivorship and mortality by integrating bioarchaeological and paleopathological data, paleoepidemiological analyses, and historical contextualization. Findings revealed changes in female longevity, with a notable increase from Roman to contemporary eras, albeit plateauing in the Middle Ages/modern period.

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

Purpose: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers.