Clinical impact of deep learning-derived intravascular ultrasound characteristics in patients with deferred coronary artery.

Prognostic markers for long-term outcomes are lacking in patients with deferred (nonculprit) coronary artery lesions. This study aimed to identify the morphological criteria for predicting adverse outcomes and validate their clinical impact. Using deep learning models, we extracted geometrical parameters and maximal attenuation (or calcium) burden index (ABI-max or CBI-max) from the intravascular ultrasound (IVUS) images of nonculprit vessels in 1115 patients.

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets.

Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker.

Sepsis is a dysregulated immune response to infection that leads to organ dysfunction and is associated with a high incidence and mortality rate. The lack of reliable biomarkers for diagnosing and prognosis of sepsis is a major challenge in its management. We aimed to investigate the potential of three-dimensional label-free CD8 + T cell morphology as a biomarker for sepsis.

Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods.

This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true-normal sinus rhythm (T-NSR), atrial fibrillation-normal sinus rhythm (AF-NSR), and clinically important arrhythmia-normal sinus rhythm (CIA-NSR).

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.

: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN.

Deep learning-based label-free hematology analysis framework using optical diffraction tomography.

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework.

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease.

Purpose: Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients.

Materials And Methods: Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions.

Machine-learning-based diagnosis of thyroid fine-needle aspiration biopsy synergistically by Papanicolaou staining and refractive index distribution.

We developed a machine learning algorithm (MLA) that can classify human thyroid cell clusters by exploiting both Papanicolaou staining and intrinsic refractive index (RI) as correlative imaging contrasts and evaluated the effects of this combination on diagnostic performance. Thyroid fine-needle aspiration biopsy (FNAB) specimens were analyzed using correlative optical diffraction tomography, which can simultaneously measure both, the color brightfield of Papanicolaou staining and three-dimensional RI distribution. The MLA was designed to classify benign and malignant cell clusters using color images, RI images, or both.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice.

Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.

Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose using the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labelling, are encoded in three-dimensional (3D) RI tomograms.

Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning.

. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research.

Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound-Based Deep Learning.

Objectives: The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)-based models for predicting the occurrence of stent underexpansion.

Background: Although post-stenting IVUS has been used to optimize percutaneous coronary intervention, there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment.

Methods: A total of 618 coronary lesions in 618 patients undergoing percutaneous coronary intervention were randomized into training and test sets in a 5:1 ratio.

Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease.

Background And Aims: Although plaque characterization by intravascular ultrasound (IVUS) is important for risk stratification, frame-by-frame analysis of a whole vascular segment is time-consuming. The aim was to develop IVUS-based algorithms for classifying attenuation and calcified plaques.

Methods: IVUS image sets of 598 coronary arteries from 598 patients were randomized into training and test sets with 5:1 ratio.

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness.

Calibration-free quantitative phase imaging using data-driven aberration modeling.

We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field.

Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning.

Aims: The aim of this study was to develop a deep learning model for classifying frames with versus without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA).

Methods And Results: A total of 602 coronary lesions from 602 angina patients were randomised into training and test sets in a 4:1 ratio. A DenseNet model was developed to classify OCT frames with or without OCT-derived TCFA.

Deep learning-based optical field screening for robust optical diffraction tomography.

In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT).

Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.

Background And Aims: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop machine learning (ML) models for predicting optical coherence tomography-derived thin-cap fibroatheromas (OCT-TCFAs).

Methods: In 517 patients with angina, 414 and 103 coronary lesions were randomized into training vs.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography.

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images.

Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.

Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80.

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells.

We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or require labeling procedures. Our method involves the synergistic employment of QPI and machine learning.