6 research outputs found
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Artificial intelligence (AI) continues to transform data analysis in many
domains. Progress in each domain is driven by a growing body of annotated data,
increased computational resources, and technological innovations. In medicine,
the sensitivity of the data, the complexity of the tasks, the potentially high
stakes, and a requirement of accountability give rise to a particular set of
challenges. In this review, we focus on three key methodological approaches
that address some of the particular challenges in AI-driven medical decision
making. (1) Explainable AI aims to produce a human-interpretable justification
for each output. Such models increase confidence if the results appear
plausible and match the clinicians expectations. However, the absence of a
plausible explanation does not imply an inaccurate model. Especially in highly
non-linear, complex models that are tuned to maximize accuracy, such
interpretable representations only reflect a small portion of the
justification. (2) Domain adaptation and transfer learning enable AI models to
be trained and applied across multiple domains. For example, a classification
task based on images acquired on different acquisition hardware. (3) Federated
learning enables learning large-scale models without exposing sensitive
personal health information. Unlike centralized AI learning, where the
centralized learning machine has access to the entire training data, the
federated learning process iteratively updates models across multiple sites by
exchanging only parameter updates, not personal health data. This narrative
review covers the basic concepts, highlights relevant corner-stone and
state-of-the-art research in the field, and discusses perspectives.Comment: This paper is accepted in IEEE CAA Journal of Automatica Sinica, Nov.
10 202
Immunolocalization of neurokinin 1 receptor in WHO grade 4 astrocytomas, oral squamous cell and urothelial carcinoma
Neurokinin-1 receptor (NK-1R) induces inflammatory reactions in peripheral tissues but its regulatory effects in target tissues is dependent on receptor signalling. Substance P (SP) has a high affinity for the NK-1R, to which it binds preferentially. We aimed to investigate the expression of NK-1R in World Health Organization (WHO) grade 4 astrocytomas as well as in oral squamous cell carcinoma (OSCC) and urothelial carcinoma, and its association with disease progression.The study included tissue samples from 19 brain astrocytomas, 40 OSCCs and 10 urothelial carcinomas. NK-1R expression was quantitatively assessed in the tumour cells using immunohistochemistry. The relationship between NK-1R expression in astrocytomas and recurrence-free interval has been explored.The results showed that the NK-1R was intensely expressed in patients with WHO grade 4 astrocytoma, OSCC and urothelial carcinoma. However, cases clinically diagnosed as a low-grade cancer showed reduced NK-1R expression.NK-1R is overexpressed in all cases of WHO grade 4 astrocytoma, OSCC and urothelial carcinoma. The ubi-quitous presence of SP/NK-1R complex during tumour development and progression suggests a possible therapeutic key strategy to use NK-1R antagonist as an adjuvant therapy in the future
Future Artificial Intelligence tools and perspectives in medicine
Purpose of review: Artificial intelligence (AI) has become popular in medical
applications, specifically as a clinical support tool for computer-aided
diagnosis. These tools are typically employed on medical data (i.e., image,
molecular data, clinical variables, etc.) and used the statistical and machine
learning methods to measure the model performance. In this review, we
summarized and discussed the most recent radiomic pipeline used for clinical
analysis. Recent findings:Currently, limited management of cancers benefits
from artificial intelligence, mostly related to a computer-aided diagnosis that
avoids a biopsy analysis that presents additional risks and costs. Most AI
tools are based on imaging features, known as radiomic analysis that can be
refined into predictive models in non-invasively acquired imaging data. This
review explores the progress of AI-based radiomic tools for clinical
applications with a brief description of necessary technical steps. Explaining
new radiomic approaches based on deep learning techniques will explain how the
new radiomic models (deep radiomic analysis) can benefit from deep
convolutional neural networks and be applied on limited data sets. Summary: To
consider the radiomic algorithms, further investigations are recommended to
involve deep learning in radiomic models with additional validation steps on
various cancer types
Magnetic resonance imaging based radiomic models of prostate cancer : A narrative review
The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis‐a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi‐institutional collaboration in producing prospectively populated and expertly labeled imaging libraries. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.open access</p