Clinical spectrum of adult-onset leukoencephalopathy with axonal spheroids and pigmented glia in individuals of Korean ancestry.

Adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP) is a rare white matter disease characterized by axonal and glial injury. Although its clinical characteristics have been described in case reports, the prevalence of CSF1R mutations in clinically suspected ALSP cases remains unclear. Herein, we analysed the frequency of CSF1R mutations in patients with probable or possible ALSP and describe the genetic, clinical, radiological, and pathological findings of ALSP cases in individuals of Korean ancestry.

Deep profiling of gene expression across 18 human cancers.

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers.

The "Hedgehog-Halo Sign" Is Associated with Gait Symptom Severity and Tap Response in Normal Pressure Hydrocephalus.

Background: Reduced cerebrospinal fluid (CSF) clearance may play a vital role in the pathogenesis of normal pressure hydrocephalus (NPH), but the radiologic marker is yet to be elucidated.

Objectives: This open-label study presents two novel neuroimaging biomarkers based on enlarged perivascular spaces (ePVS) of the sub-insular territory: the Hedgehog and Hedgehog-Halo (H-H) sign, designed to predict gait symptom severity and tap response in NPH.

Methods: We retrospectively reviewed 203 patients with possible NPH with baseline magnetic resonance imaging and gait analyses before and after lumbar puncture (LP).

Myelin oligodendrocyte glycoprotein antibody-associated disease with histopathologic features of primary CNS angiitis without demyelination: Case report and literature review.

Primary angiitis of the central nervous system (PACNS) is a rare inflammatory disease that affects both small- and medium-sized vessels of the CNS, while myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is a novel antibody-mediated inflammatory demyelinating disorder that causes damage to the myelin in CNS. We report a case diagnosed as MOGAD due to a history of recurrent myelitis, brain lesions, and positive anti-MOG, but the brain biopsy showed vasculitis without demyelination.

Autonomous real-time control for membrane capacitive deionization.

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy.

Textbook Outcome of Delta-Shaped Anastomosis in Minimally Invasive Distal Gastrectomy for Gastric Cancer in 4,505 Consecutive Patients.

Purpose: Textbook outcome is a comprehensive measure used to assess surgical quality and is increasingly being recognized as a valuable evaluation tool. Delta-shaped anastomosis (DA), an intracorporeal gastroduodenostomy, is a viable option for minimally invasive distal gastrectomy in patients with gastric cancer. This study aims to evaluate the surgical outcomes and calculate the textbook outcome of DA.

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation.

A deep profile of gene expression across 18 human cancers.

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

Background: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations.

Isolating salient variations of interest in single-cell data with contrastiveVI.

Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to deconvolve variations enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations.

Fostering transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (edical ccept rriever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation.

Dissection of medical AI reasoning processes via physician and generative-AI collaboration.

Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning-based medical image AI.

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes.

Predictive and robust gene selection for spatial transcriptomics.

A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data.

Factors associated with distress among female cancer survivors at the workplace: A cross-sectional study.

Objectives: This study aimed to investigate the levels of fatigue, social support, spiritual well-being, and distress of female cancer survivors at the workplace, and identify factors associated with distress.

Methods: One hundred and eighty-two working female cancer survivors participated from the outpatient ward in two medical institutions in South Korea and they completed questionnaires assessing their general characteristics, fatigue, social support (colleagues and superiors), and spiritual well-being distress (existential and religious well-being). The data were analyzed using descriptive statistics, T-test, one-way ANOVA, correlation, and multiple linear regression with SPSS /WIN18 version.

Interpretable machine learning prediction of all-cause mortality.

Background: Unlike linear models which are traditionally used to study all-cause mortality, complex machine learning models can capture non-linear interrelations and provide opportunities to identify unexplored risk factors. Explainable artificial intelligence can improve prediction accuracy over linear models and reveal great insights into outcomes like mortality. This paper comprehensively analyzes all-cause mortality by explaining complex machine learning models.

Explaining a series of models by propagating Shapley values.

Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to compute or are not capable of explaining a distributed series of models where each model is owned by a separate institution. The latter is particularly important because it often arises in finance where explanations are mandated.

An automatic integrative method for learning interpretable communities of biological pathways.

Although knowledge of biological pathways is essential for interpreting results from computational biology studies, the growing number of pathway databases complicates efforts to efficiently perform pathway analysis due to high redundancies among pathways from different databases, and inconsistencies in how pathways are created and named. We introduce the PAthway Communities (PAC) framework, which reconciles pathways from different databases and reduces pathway redundancy by revealing informative groups with distinct biological functions. Uniquely applying the Louvain community detection algorithm to a network of 4847 pathways from KEGG, REACTOME and Gene Ontology databases, we identify 35 distinct and automatically annotated communities of pathways and show that they are consistent with expert-curated pathway categories.

A cost-aware framework for the development of AI models for healthcare applications.

Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care medicine, developing accurate AI models, which typically require inputs for many clinical variables, may be impractical. Here we report a model-agnostic cost-aware AI (CoAI) framework for the development of predictive models that optimize the trade-off between prediction performance and feature cost.