Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).

Background: Endoscopic submucosal dissection (ESD) is a crucial yet challenging multi-phase procedure for treating early gastrointestinal cancers. This study developed an artificial intelligence (AI)-based automated surgical workflow recognition model for esophageal ESD and proposed an innovative training program based on esophageal ESD videos with or without AI labels to evaluate its effectiveness for trainees.

Methods: We retrospectively analyzed complete ESD videos collected from seven hospitals worldwide between 2016 and 2024.

Supramolecular assemblies with spatio-temporal sequential drug delivery capability treat spinal cord injury via neuroprotection and immunoregulation.

Spinal cord injury (SCI) can result in irreversible motor and sensory deficits. However, up to data, clinical first-line drugs have ambiguous benefits and debilitating side effects, mainly due to the insufficient accumulation, poor physiological barrier penetration, and lack of spatio-temporal controlled release at lesion tissue. Herein, we proposed a supramolecular assemblies composed of hyperbranched polymer-formed core/shell structure through host-guest interactions.

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks.

Background And Objectives: There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system.

Methods: A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.

Artificial intelligence for diagnosing gastric lesions under white-light endoscopy.

Background: The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis.

Deep regression using Tc-DTPA dynamic renal imaging for automatic calculation of the glomerular filtration rate.

Objectives: To develop and evaluate an artificial intelligence (AI) system that can automatically calculate the glomerular filtration rate (GFR) from dynamic renal imaging without manually delineating the regions of interest (ROIs) of kidneys and the corresponding background.

Methods: This study was a single-center retrospective analysis of the data of 14,634 patients who underwent Tc-DTPA dynamic renal imaging. Two systems based on convolutional neural networks (CNN) were developed and evaluated: sGFR predicts the radioactive counts of ROIs and calculates GFR using the Gates equation and sGFR directly predicts GFR from dynamic renal imaging without using other information.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

Objectives: Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data.

TeachMe: a web-based teaching system for annotating abdominal lymph nodes.

The detection and characterization of lymph nodes through interpreting abdominal medical images are significant for diagnosing and treating colorectal cancer recurrence. However, interpreting abdominal medical images manually is labor-intensive and time-consuming. The related radiology education has many limitations as well.

An automatic fine-grained skeleton segmentation method for whole-body bone scintigraphy using atlas-based registration.

Purpose: Whole-body bone scintigraphy (WBS) is one of the common imaging methods in nuclear medicine. It is a time-consuming, tedious, and error-prone issue for physicians to determine the location of bone lesions which is important for the qualitative diagnosis of bone lesions. In this paper, an automatic fine-grained skeleton segmentation method for WBS is developed.

Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.

Background And Purpose: The preoperative lymph node (LN) status is important for the treatment of colorectal cancer (CRC). Here, we established and validated a deep learning (DPL) model for predicting lymph node metastasis (LNM) in CRC.

Materials And Methods: A total of 423 CRC patients were divided into cohort 1 (training set, n = 238, testing set, n = 101) and cohort 2 (validation set, n = 84).

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.

Purpose: The robust and automatic segmentation of the pulmonary lobe is vital to surgical planning and regional image analysis of pulmonary related diseases in real-time Computer Aided Diagnosis systems. While a number of studies have examined this issue, the segmentation of unclear borders of the five lobes of the lung remains challenging because of incomplete fissures, the diversity of anatomical pulmonary information, and obstructive lesions caused by pulmonary diseases. This study proposes a model called Regularized Pulmonary Lobe Segmentation Network to accurately predict the lobes as well as the borders.

Automatic airway tree segmentation based on multi-scale context information.

Purpose: Airway tree segmentation plays a pivotal role in chest computed tomography (CT) analysis tasks such as lesion localization, surgical planning, and intra-operative guidance. The remaining challenge is to identify small bronchi correctly, which facilitates further segmentation of the pulmonary anatomies.

Methods: A three-dimensional (3D) multi-scale feature aggregation network (MFA-Net) is proposed against the scale difference of substructures in airway tree segmentation.

Incorporating historical sub-optimal deep neural networks for dose prediction in radiotherapy.

As the main treatment for cancer patients, radiotherapy has achieved enormous advancement over recent decades. However, these achievements have come at the cost of increased treatment plan complexity, necessitating high levels of expertise experience and effort. The accurate prediction of dose distribution would alleviate the above issues.

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of Tc-MDP bone scintigraphy and evaluated its diagnostic performance of bone metastasis.

Automated diagnosis of bone metastasis based on multi-view bone scans using attention-augmented deep neural networks.

Bone scintigraphy is accepted as an effective diagnostic tool for whole-body examination of bone metastasis. However, the manual analysis of bone scintigraphy images requires extensive experience and is exhausting and time-consuming. An automated diagnosis system for such images is therefore much desired.

MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks.

The accurate identification of malignant lung nodules using computed tomography (CT) screening images is vital for the early detection of lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has the ability to capture intra-tumoral heterogeneity. Deep learning methods have obtained promising results for the malignancy identification problem; however, two substantial challenges still remain.

Dose prediction using a deep neural network for accelerated planning of rectal cancer radiotherapy.

Purpose: To apply a deep neural network to predict dose distributions of rectal cancer patients for accelerated volume modulated arc technique (VMAT) planning.

Materials And Methods: Computed tomography scans and approved VMAT plans together with Dose of 187 patients treated from February 2018 to April 2019 were randomly selected for this retrospective study. The deep neural network DeepLabv3+ was applied for dose distribution prediction.

Multi-task learning for the segmentation of organs at risk with label dependence.

Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk in CT images. We train an encoder-decoder network for two tasks in parallel.

Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy.

Background And Purpose: Manual delineation of clinical target volumes (CTVs) and organs at risk (OARs) is time-consuming, and automatic contouring tools lack clinical validation. We aimed to construct and validate the use of convolutional neural networks (CNNs) to set better contouring standards for rectal cancer radiotherapy.

Materials And Methods: We retrospectively collected and evaluated computed tomography (CT) scans of 199 rectal cancer patients treated at our hospital from February 2018 to April 2019.

Automated segmentation of macular edema in OCT using deep neural networks.

Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties.

Automated diagnosis of breast ultrasonography images using deep neural networks.

Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired.

Automated retinopathy of prematurity screening using deep neural networks.

Background: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Automated ROP detection system is urgent and it appears to be a safe, reliable, and cost-effective complement to human experts.

Methods: An automated ROP detection system called DeepROP was developed by using Deep Neural Networks (DNNs).

Characterization of fowl adenoviruses isolated between 2007 and 2014 in China.

Forty-three fowl adenovirus (FAdV) strains were isolated in China from 2007 to 2014 from poultry and ostriches with inclusion body hepatitis (IBH) and hydropericardium syndrome (HPS). Phylogenetic analysis showed that 28/43 strains clustered into Fowl aviadenovirus D (FAdV-D) and 9/43 strains clustered into FAdV-E. FAdV-C included three isolates of ostrich origin, one of goose origin and two of chicken origin.

Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks.

The competitive layer model (CLM) can be described by an optimization problem. The problem can be further formulated by an energy function, called the CLM energy function, in the subspace of nonnegative orthant. The set of minimum points of the CLM energy function forms the set of solutions of the CLM problem.

Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks.

The concepts of permitted and forbidden sets enable a new perspective of the memory in neural networks. Such concepts exhibit interesting dynamics in recurrent neural networks. This paper studies the basic theories of permitted and forbidden sets of the linear threshold discrete-time recurrent neural networks.

Output convergence analysis for a class of delayed recurrent neural networks with time-varying inputs.

This paper studies the output convergence of a class of recurrent neural networks with time-varying inputs. The model of the studied neural networks has different dynamic structure from that in the well known Hopfield model, it does not contain linear terms. Since different structures of differential equations usually result in quite different dynamic behaviors, the convergence of this model is quite different from that of Hopfield model.