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Radiology Radiology

Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.

In British journal of cancer ; h5-index 89.0

Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.

Guo Jiamin, Hu Junjie, Zheng Yichen, Zhao Shuang, Ma Ji

2023-Mar-04

General General

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.

In Scientific reports ; h5-index 158.0

How do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of 'animal space' based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a putative mechanism enabling the emergence of new, abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block to include prior knowledge, and to derive context knowledge from novel input. Thus, our model provides a new tool to complement contemporary deep learning approaches on the road towards artificial general intelligence.

Stoewer Paul, Schilling Achim, Maier Andreas, Krauss Patrick

2023-Mar-04

Public Health Public Health

Associations of Modified Healthy Aging Index With Major Adverse Cardiac Events, Major Coronary Events, and Ischemic Heart Disease.

In Journal of the American Heart Association ; h5-index 70.0

Background The Healthy Aging Index (HAI) has been regarded as useful in capturing the health status of multiple organ systems. However, to what extent the HAI is associated with major cardiovascular events remains largely unknown. The authors constructed a modified HAI (mHAI) to quantify the association of physiological aging with major vascular events and explored how the effects of a healthy lifestyle can modify this association. Methods and Results The participants with either missing values of any individual mHAI component or major illnesses such as heart attack, angina and stroke, and self-reported cancer at baseline were excluded. The mHAI components include systolic blood pressure, reaction time, forced vital capacity, serum cystatin c, and serum glucose. The authors used Cox proportional hazard models to quantify the association of mHAI with major adverse cardiac events, major coronary events, and ischemic heart disease. Cumulative incidence at 5 and 10 years was estimated, and joint analyses were stratified by age group and 4 mHAI categories. The mHAI was significantly correlated with major cardiovascular events, which is a better reflection of the aging level of the body than chronological age. An mHAI was calculated in 338 044 participants aged 38 to 73 years in the UK Biobank. Each point increase in the mHAI was associated with a 44% higher risk of major adverse cardiac events (adjusted hazard ratio [aHR], 1.44 [95% CI, 1.40-1.49]), 44% higher risk of major coronary events (aHR, 1.44 [95% CI, 1.40-1.48]), and 36% higher risk of ischemic heart disease (aHR, 1.36 [95% CI, 1.33-1.39]). The percentage of population-attribution risk was 51% (95% CI, 47-55) for major adverse cardiac events, 49% (95% CI, 45-53) for major coronary events, and 47% (95% CI, 44-50) for ischemic heart disease, which means that a substantial portion of these events could be prevented. Systolic blood pressure was the factor most significantly associated with major adverse cardiac events (aHR, 1.94 [95% CI, 1.82-2.08]; percentage of population-attribution risk, 36%), major coronary events (aHR, 2.01 [95% CI, 1.85-2.17]; percentage of population-attribution risk, 38%), and ischemic heart disease (aHR, 1.80 [95% CI, 1.71-1.89]; percentage of population-attribution risk, 32%). A healthy lifestyle significantly attenuated mHAI associations with incidence of vascular events. Conclusions Our findings indicate that higher mHAI is associated with increased major vascular events. A healthy lifestyle may attenuate these associations.

Huang Ninghao, Zhuang Zhenhuang, Song Zimin, Wang Wenxiu, Li Yueying, Zhao Yimin, Xiao Wendi, Dong Xue, Jia Jinzhu, Liu Zhonghua, Smith Caren E, Huang Tao

2023-Mar-04

biomarkers, cardiovascular disease, healthy aging index

Pathology Pathology

Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts.

In Seminars in diagnostic pathology

Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.

Albahra Samer, Gorbett Tom, Robertson Scott, D’Aleo Giana, Kumar Sushasree Vasudevan Suseel, Ockunzzi Samuel, Lallo Daniel, Hu Bo, Rashidi Hooman H

2023-Feb-16

Artificial intelligence, Laboratory medicine, Learning, Machine learning, Pathology, Predictive modeling, Supervised

Radiology Radiology

Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma.

In Seminars in nuclear medicine ; h5-index 30.0

Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.

Veziroglu Eren M, Farhadi Faraz, Hasani Navid, Nikpanah Moozhan, Roschewski Mark, Summers Ronald M, Saboury Babak

2023-Mar-02

General General

Computational biology: Role and scope in taming antimicrobial resistance.

In Indian journal of medical microbiology

BACKGROUND : Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of computational biology there are tools and techniques available to address and solve the disease management issues in the field of clinical microbiology. The sequencing techniques, structural biology and machine learning can be implemented collectively to tackle infectious diseases e.g. for the diagnosis, epidemiological typing, pathotyping, antimicrobial resistance detection as well as the discovery of novel drugs and vaccine biomarkers.

OBJECTIVES : The present review is a narrative review representing a comprehensive literature-based assessment regarding the use of whole genome sequencing, structural biology and machine learning for the diagnosis, molecular typing and antibacterial drug discovery.

CONTENT : Here, we seek to present an overview of molecular and structural basis of resistance to antibiotics, while focusing on the recent bioinformatics approaches in whole genome sequencing and structural biology. The application of next generation sequencing in management of bacterial infections focusing on investigation of microbial population diversity, genotypic resistance testing and scope for the identification of targets for novel drug and vaccine candidates, has been addressed along with the use of structural biophysics and artificial intelligence.

Sharma Priyanka, Dahiya Sushila, Kaur Punit, Kapil Arti

2023

Antibiotic resistance, Computational biology, Genome sequencing, Machine learning, S. Typhi