Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.

Purpose: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.

Methods: A total of 562 patients from four centers were divided into the training cohort, validation cohort and test cohort.

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depression patterns and status of depressive symptoms in adolescents could help identify early intervention targets. Despite the growing use of artificial intelligence for diagnosis and prediction of mental health conditions, the traditional centralized machine learning methods require aggregating adolescents' data; this raises concerns about confidentiality and privacy, which hampers the clinical application of machine learning algorithms.

A remarkable membrane-permeable fluorescent probe for real-time imaging of mitochondrial SO with high fidelity during ferroptosis.

Mitochondrial sulfur dioxide (SO) plays a double-edged role in cells, and the real-time and tracing of its dynamic behaviors to elucidate its complicated functions in detail is of great significance. Here, we developed a simple mitochondria-targeted fluorescent probe ZW for tracing SO with good membrane permeability. In probe ZW, the 1-phenylpyrrolidine-decorated benzopyrylium unit is employed as the selective response site for SO.

A single-molecule fluorescent probe for visualizing viscosity and hypoxia in lysosomes and zebrafish embryos.

Viscosity and hypoxia, as microenvironment parameters, play important roles in maintaining normal biological processes and homeostasis. Therefore, simultaneous and sensitive detection of these elements with simple and effective methods could offer precise information in biology. Here, we report a two-site lysosome-targeting fluorescent probe, NVP, for monitoring viscosity and nitroreductase with dual emission channels (emission shift is 86 nm).

Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma .

Background: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.

Aim: The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image.

Development of a polarity-sensitive ratiometric fluorescent probe based on the intramolecular reaction of spiro-oxazolidine and its applications for in situ visualizing the fluctuations of polarity during ER stress.

Polarity is a vital element in endoplasmic reticulum (ER) microenvironment, and its variation is closely related to many physiological and pathological activities of ER, so it is necessary to trace fluctuations of polarity in ER. However, most of fluorescent probes for detecting polarity dependent on the changes of single emission, which could be affected by many factors and cause false signals. Ratiometric fluorescent probe with "built-in calibration" can effectively avoid detection errors.

A pH-response-based fluorescent probe for detecting the mitophagy process by tracing changes in colocalization coefficients.

Mitochondria are not only the center of energy metabolism but also involved in regulating cellular activities. Quality and quantity control of mitochondria is therefore essential. Mitophagy is a lysosome-dependent process to clear dysfunctional mitochondria, and abnormal mitophagy can cause metabolic disorders.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Background: Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images.

Home-based tele-rehabilitation versus hospital-based outpatient rehabilitation for pain and function after initial total knee arthroplasty: A systematic review and meta-analysis.

Background: This systematic review and meta-analysis aims to compare the effectiveness of home-based tele-rehabilitation programs with hospital-based rehabilitation programs in improving pain and function at various time points (≤6 weeks, ≤14 weeks, and ≤ 52 weeks) following the initial total knee arthroplasty.

Methods: This study used PRISMA and AMSTAR reporting guidelines. We systematically searched 5 databases (PubMed, Embase, Web of Science, Cochrane Library, and Medline) to identify randomized controlled trials published from January 1, 2019, to January 1, 2023.

Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.

Background: Although there are many studies on the prognostic factors of left ventricular myocardial noncompaction (LVNC), the determinants are varied and not entirely consistent. This study aimed to build predictive models using radiomics features and machine learning to predict major adverse cardiovascular events (MACEs) in patients with LVNC.

Methods: In total, 96 patients with LVNC were included and randomly divided into training and test cohorts.

Hierarchical-Instance Contrastive Learning for Minority Detection on Imbalanced Medical Datasets.

Deep learning methods are often hampered by issues such as data imbalance and data-hungry. In medical imaging, malignant or rare diseases are frequently of minority classes in the dataset, featured by diversified distribution. Besides that, insufficient labels and unseen cases also present conundrums for training on the minority classes.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study.

Objective: To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).

Methods: A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121.

Anatomically Guided Cross-Domain Repair and Screening for Ultrasound Fetal Biometry.

Ultrasound based estimation of fetal biometry is extensively used to diagnose prenatal abnormalities and to monitor fetal growth, for which accurate segmentation of the fetal anatomy is a crucial prerequisite. Although deep neural network-based models have achieved encouraging results on this task, inevitable distribution shifts in ultrasound images can still result in severe performance drop in real world deployment scenarios. In this article, we propose a complete ultrasound fetal examination system to deal with this troublesome problem by repairing and screening the anatomically implausible results.

Computational fluid dynamics can detect changes in airway resistance for patients after COVID-19 infection.

Infection with COVID-19 can cause severe complication in the respiratory system, which may be related to increased respiratory resistance. Computational fluid dynamics(CFD) was used in this study to calculate the airway resistance based on the airway anatomy and a common air flowrate. The correlation between airway resistance and COVID-19 prognosis was then investigated.

Self-supervised anomaly detection, staging and segmentation for retinal images.

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images.

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

Objectives: To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).

Methods: Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models.

Experimental Boson Sampling Enabling Cryptographic One-Way Function.

Boson sampling is a computational problem, which is commonly believed to be a representative paradigm for attaining the milestone of quantum advantage. So far, massive efforts have been made to the experimental large-scale boson sampling for demonstrating this milestone, while further applications of the machines remain a largely unexplored area. Here, we investigate experimentally the efficiency and security of a cryptographic one-way function that relies on coarse-grained boson sampling, in the framework of a photonic boson-sampling machine fabricated by a femtosecond laser direct writing technique.

Deep learning in bladder cancer imaging: A review.

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images.

Direct Observation of Dynamically Localized Quantum Optical States.

Quantum-correlated biphoton states play an important role in quantum communication and processing, especially considering the recent advances in integrated photonics. However, it remains a challenge to flexibly transport quantum states on a chip, when dealing with large-scale sophisticated photonic designs. The equivalence between certain aspects of quantum optics and solid-state physics makes it possible to utilize a range of powerful approaches in photonics, including topologically protected boundary states, graphene edge states, and dynamic localization.

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.

Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed.

Application of deep learning to identify ductal carcinoma and microinvasion of the breast using ultrasound imaging.

Background: The treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm.