Pathology

ABO blood group system and risk of positive surgical margins in patients treated with robot-assisted radical prostatectomy: results in 1114 consecutive patients.

To test the hypothesis of associations between the ABO blood group system (ABO-bg) and prostate cancer (PCa) features in the surgical specimen of patients treated with robot-assisted radical prostatectomy (RARP). Between January 2013 and October 2020, 1114 patients were treated with RARP. Associations of ABO-bg with specimen pathological features were evaluated by statistical methods. Overall, 305...

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films.

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically u...

An Automatic Biopsy Needle Detection and Segmentation on Ultrasound Images Using a Convolutional Neural Network.

Needle visualization in the ultrasound image is essential to successfully perform the ultrasound-guided core needle biopsy. Automatic needle detection...

Use of Convolutional Neural Networks for the Detection of u-Serrated Patterns in Direct Immunofluorescence Images to Facilitate the Diagnosis of Epidermolysis Bullosa Acquisita.

The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of ...

WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method.

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly effici...

Construction and Validation of a Lung Cancer Diagnostic Model Based on 6-Gene Methylation Frequency in Blood, Clinical Features, and Serum Tumor Markers.

Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate o...

Quality control stress test for deep learning-based diagnostic model in digital pathology.

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histologi...

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.

AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interp...

Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.

Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and chara...

Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.

Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and pati...

Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradatio...

Tip estimation approach for concentric tube robots using 2D ultrasound images and kinematic model.

Concentric tube robot (CTR) is an efficient approach for minimally invasive surgery (MIS) and diagnosis due to its small size and high dexterity. To m...

The Impact of New Surgical Techniques on Geographical Unwarranted Variation: The Case of Benign Hysterectomy.

Since the 1980s, the international literature has reported variations for healthcare services, especially for elective ones. Variations are positive i...

Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery.

BACKGROUND: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B ...

Deep learning of HIV field-based rapid tests.

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not ...

Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection.

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase ...

Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or ...

Deep learning for diagnosis and survival prediction in soft tissue sarcoma.

BACKGROUND: Clinical management of soft tissue sarcoma (STS) is particularly challenging. Here, we used digital pathology and deep learning (DL) for d...

Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks.

BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, mi...

Deep learning for discovering pathological continuum of crypts and evaluating therapeutic effects: An implication for in vivo preclinical study.

Applying deep learning to the field of preclinical in vivo studies is a new and exciting prospect with the potential to unlock decades worth of underu...