PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images.

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver condition characterized by excessive hepatic fat accumulation. Early diagnosis is crucial as NAFLD can progress to more severe conditions like steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma without timely intervention. While liver biopsy remains the gold standard for NAFLD assessment, abdominal ultrasound (US) imaging has emerged as a widely adopted non-invasive modality due to convenience and low cost.

Early prediction of radiotherapy outcomes in pharyngeal cancer using deep learning on baseline [18F]Fluorodeoxyglucose positron emission Tomography/Computed tomography.

Objectives: This study aimed to develop an integrated segmentation-free deep learning (DL) framework to predict multiple aspects of radiotherapy outcome in pharyngeal cancer patients by analyzing pretreatment 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET/CT).

Methods: We utilized baseline 18F-FDG-PET/CT scans from patients newly diagnosed with oropharyngeal or hypopharyngeal cancer. The study cohort comprised 162 patients for training and 32 for validation, all of whom completed definitive chemoradiotherapy or radiotherapy for organ-preservation.

A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography.

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%.

Psoas muscle area is an independent survival prognosticator in patients undergoing surgery for long-bone metastases.

Background: Predictive analytics is gaining popularity as an aid to treatment planning for patients with bone metastases, whose expected survival should be considered. Decreased psoas muscle area (PMA), a morphometric indicator of suboptimal nutritional status, has been associated with mortality in various cancers, but never been integrated into current survival prediction algorithms (SPA) for patients with skeletal metastases. This study investigates whether decreased PMA predicts worse survival in patients with extremity metastases and whether incorporating PMA into three modern SPAs (PATHFx, SORG-NG, and SORG-MLA) improves their performance.

Development of a rapid aptamer-chemiluminescence sensor for detecting glyphosate pesticide residue in soybeans.

Glyphosate (GLY) is a widely used herbicide worldwide, particularly in cultivating genetically modified soybeans resistant to GLY. However, routine multi-residue analysis does not include GLY due to the complexity of soybean matrix components that can interfere with the analysis. This study presented the development of an aptamer-based chemiluminescence (Apt-CL) sensor for rapidly screening GLY pesticide residue in soybeans.

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy.

Aims: Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.

Imaging prediction of mutation in patients with rectal cancer through deep metric learning using pretreatment [F]Fluorodeoxyglucose positron emission tomography/computed tomography.

Objectives: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML).

Methods: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis.

Stratifying High-Risk Thyroid Nodules Using a Novel Deep Learning System.

Introduction: The current ultrasound scan classification system for thyroid nodules is time-consuming, labor-intensive, and subjective. Artificial intelligence (AI) has been shown to increase the accuracy of predicting the malignancy rate of thyroid nodules. This study aims to demonstrate the state-of-the-art Swin Transformer to classify thyroid nodules.

Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.

Purpose: This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound.

Methods: The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny.

Clinical outcomes of handmade polytetrafluoroethylene trileaflet-valved conduit used for pulmonary valve replacement.

Objectives: To mitigate the shortage of homograft sources, the use of handmade trileaflet expanded polytetrafluoroethylene valves in pulmonary valve replacement has shown excellent results from multicentre studies conducted in Japan. However, world-wide data outside Japan are relatively insufficient. This study presents the long-term results of a single surgeon's use of flipped-back trileaflet method in a 10-year case series.

An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification.

Background And Objective: Lung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection and diagnosis. It provided a complete three-dimensional (3-D) chest image with a high resolution.

CheXGAT: A disease correlation-aware network for thorax disease diagnosis from chest X-ray images.

Chest X-ray (CXR) imaging is one of the most common diagnostic imaging techniques in clinical diagnosis and is usually used for radiological examinations to screen for thorax diseases. In this paper, we propose a novel computer-aided diagnosis (CAD) system based on a hybrid deep learning model composed of a convolutional neural network (CNN) and a graph neural network (GNN). The system is intended to explore implicit correlations between thorax diseases to aid in the multilabel chest X-ray image classification task, which we term ‶CheXGAT‶.

Intraoperative Detection of Surgical Gauze Using Deep Convolutional Neural Network.

During laparoscopic surgery, surgical gauze is usually inserted into the body cavity to help achieve hemostasis. Retention of surgical gauze in the body cavity may necessitate reoperation and increase surgical risk. Using deep learning technology, this study aimed to propose a neural network model for gauze detection from the surgical video to record the presence of the gauze.

Effect of physical activity in a weight loss program on circulating total ANGPTL8 concentrations in northern Americans with obesity: A prospective randomized controlled trial.

Background And Aims: The primary goals of this study were to clarify 1) the effect of weight loss by lifestyle intervention on circulating total angiopoietin-like protein 8 (ANGPTL8), and 2) the role of physical activity on serum total ANGPTL8 in northern Americans with obesity but without diabetes.

Methods And Results: A total of 130 subjects with body mass index (BMI) ≧ 35 kg/m but without diabetes were recruited, and 121 subjects completed a weight loss program for data analysis. Abdominal adipose tissue was determined by non-contrast computed tomography (CT).

One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image.

Background And Objective: Lung cancer is the most common cause of cancer-related death in the world. Low-dose computed tomography (LDCT) is a widely used modality in lung cancer detection. The nodule is an abnormal tissue and may evolve into lung cancer.

Development and validation of a deep learning-based algorithm for colonoscopy quality assessment.

Background: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure.

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs).

Upper endoscopy photodocumentation quality evaluation with novel deep learning system.

Objectives: Visualization and photodocumentation during endoscopy procedures are suggested to be one indicator for endoscopy performance quality. However, this indicator is difficult to measure and audit manually in clinical practice. Artificial intelligence (AI) is an emerging technology that may solve this problem.

Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

Background: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach.

Improving Algorithm for the Alignment of Consecutive, Whole-Slide, Immunohistochemical Section Images.

Background: Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images.

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretreatment decisions in patients with cancers. We proposed a novel deep prediction method with a size-related damper block for nodal metastasis (Nmet) identification from the primary tumor in lung cancer generated by gemstone spectral imaging (GSI) dual-energy computer tomography (CT).