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Machine learning techniques for non-destructive estimation of plum fruit weight.
Sci Rep
January 2025
Crop and Horticultural Science Research Department, Mazandaran Agricultural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Tajrish, Iran.
Plum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions.
View Article and Find Full Text PDFPrognostic implications and therapeutic opportunities related to CAF subtypes in CMS4 colorectal cancer: insights from single-cell and bulk transcriptomics.
Apoptosis
January 2025
Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Cancer-associated fibroblasts (CAFs) significantly influence tumor progression and therapeutic resistance in colorectal cancer (CRC). However, the distributions and functions of CAF subpopulations vary across the four consensus molecular subtypes (CMSs) of CRC. This study performed single-cell RNA and bulk RNA sequencing and revealed that myofibroblast-like CAFs (myCAFs), tumor-like CAFs (tCAFs), inflammatory CAFs (iCAFs), CXCL14CAFs, and MTCAFs are notably enriched in CMS4 compared with other CMSs of CRC.
View Article and Find Full Text PDFPrediction of pulmonary embolism by an explainable machine learning approach in the real world.
Sci Rep
January 2025
Department of Respiratory and Critical Care Medicine, Changhai Hospital, The Second Military Medical University, Shanghai, People's Republic of China.
In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020.
View Article and Find Full Text PDFOpen challenges and opportunities in federated foundation models towards biomedical healthcare.
BioData Min
January 2025
School of Computer Science, Fudan University, Shanghai, China.
This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions.
View Article and Find Full Text PDFIntroduction to Artificial Intelligence (AI) and Machine Learning (ML) in Pathology & Medicine: Generative & Non-Generative AI Basics.
Mod Pathol
January 2025
Department of Pathology, University of Pittsburgh Medical Center, PA, USA; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address:
This manuscript serves as an introduction to a comprehensive seven-part review article series on artificial intelligence (AI) and machine learning (ML) and their current and future influence within pathology and medicine. This introductory review provides a comprehensive grasp of this fast-expanding realm and its potential to transform medical diagnosis, workflow, research, and education. Fundamental terminology employed in AI-ML is covered using an extensive dictionary.
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