Object Detection in Laparoscopic Surgery: A Comparative Study of Deep Learning Models on a Custom Endometriosis Dataset.

Laparoscopic surgery for endometriosis presents unique challenges due to the complexity of and variability in lesion appearances within the abdominal cavity. This study investigates the application of deep learning models for object detection in laparoscopic videos, aiming to assist surgeons in accurately identifying and localizing endometriosis lesions and related anatomical structures. A custom dataset was curated, comprising of 199 video sequences and 205,725 frames.

Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning.

: The timely diagnostics of bladder cancer is still a challenge in clinical settings. The reliability of conventional testing methods does not reach desirable accuracy and sensitivity, and it has an invasive nature. The present study examines the application of machine learning to improve bladder cancer diagnostics by integrating miRNA expression levels, demographic routine laboratory test results, and clinical data.

From classical dualistic antagonism to hormone synergy: potential of overlapping action of glucagon, insulin and GLP-1 for the treatment of diabesity.

The increasing prevalence of 'diabesity', a combination of type 2 diabetes and obesity, poses a significant global health challenge. Unhealthy lifestyle factors, including poor diet, sedentary behaviour, and high stress levels, combined with genetic and epigenetic factors, contribute to the diabesity epidemic. Diabesity leads to various significant complications such as cardiovascular diseases, stroke, and certain cancers.

A Data-Driven Framework for Clinical Decision Support Systems in Positive Airway Pressure and Oxygen Titration.

Background: Current obstructive sleep apnea treatment relies on manual PAP titration, but it has limitations. Complex interactions during titration and variations in SpO data accuracy pose challenges. Patients with co-occurring chronic hypercapnia may require precise oxygen titration.

Multi-Loss U-Net Reformulation as an Efficient Solution to the Colony-Forming Unit Counting Problem.

U-Net is undoubtedly the most cited and popularized deep learning architecture in the biomedical domain. Starting with image, volume, or video segmentation in numerous practical applications, such as digital pathology, and continuing to Colony-Forming Unit (CFU) segmentation, new emerging areas require an additional U-Net reformulation to solve some inherent inefficiencies of a simple segmentation-tailored loss function, such as the Dice Similarity Coefficient, being applied at the training step. One of such areas is segmentation-driven CFU counting, where after receiving a segmentation output map one should count all distinct segmented regions belonging to different detected microbial colonies.

Multispectral Imaging Analysis of Skin Lesions in Patients with Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is a rare disease, affecting around 1 in 3500 individuals in the general population. The rarity of the disease contributes to the scarcity of the available diagnostic and therapeutic approaches. Multispectral imaging is a non-invasive imaging method that shows promise in the diagnosis of various skin diseases.

Tools for classification of growing/non-growing bacterial colonies using laser speckle imaging.

Prior research has indicated the feasibility of assessing growth-associated activity in bacterial colonies through the application of laser speckle imaging techniques. A subpixel correlation method was employed to identify variations in sequential laser speckle images, thereby facilitating the visualization of specific zones indicative of microbial growth within the colony. Such differentiation between active (growing) and inactive (non-growing) bacterial colonies holds considerable implications for medical applications, like bacterial response to certain drugs or antibiotics.

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation.

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains.

Comparative Analysis of Predictive Interstitial Glucose Level Classification Models.

Background: New methods of continuous glucose monitoring (CGM) provide real-time alerts for hypoglycemia, hyperglycemia, and rapid fluctuations of glucose levels, thereby improving glycemic control, which is especially crucial during meals and physical activity. However, complex CGM systems pose challenges for individuals with diabetes and healthcare professionals, particularly when interpreting rapid glucose level changes, dealing with sensor delays (approximately a 10 min difference between interstitial and plasma glucose readings), and addressing potential malfunctions. The development of advanced predictive glucose level classification models becomes imperative for optimizing insulin dosing and managing daily activities.

Corrigendum: Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests.

[This corrects the article DOI: 10.3389/fmicb.2023.

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests.

Rapid identification of effective antibiotic treatment is crucial for increasing patient survival and preventing the formation of new antibiotic-resistant bacteria due to preventative antibiotic use. Currently utilized "gold standard" methods require 16-24 h to determine the most appropriate antibiotic for the patient's treatment. The proposed technique of laser speckle imaging with subpixel correlation analysis allows for identifying dynamics and changes in the zone of inhibition, which are impossible to observe with classical methods.

Use of the speckle imaging sub-pixel correlation analysis in revealing a mechanism of microbial colony growth.

The microbial colony growth is driven by the activity of the cells located on the edges of the colony. However, this process is not visible unless specific staining or cross-sectioning of the colony is done. Speckle imaging technology is a non-invasive method that allows visualization of the zones of increased microbial activity within the colony.

Multi-Class CNN for Classification of Multispectral and Autofluorescence Skin Lesion Clinical Images.

In this work, we propose to use an artificial neural network to classify limited data of clinical multispectral and autofluorescence images of skin lesions. Although the amount of data is limited, the deep convolutional neural network classification of skin lesions using a multi-modal image set is studied and proposed for the first time. The unique dataset consists of spectral reflectance images acquired under 526 nm, 663 nm, 964 nm, and autofluorescence images under 405 nm LED excitation.

Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation.

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets.

The oxygenation module: the missing link in using sleep apnea devices to treat COVID-19 pneumonia at home.

Background: The study aims at solving the problem with the limitations of the homecare CPAP equipment such as sleep apnea devices in the treatment of COVID-19 pneumonia. By adding an advanced, rapid-to-produce oxygenation module to existing CPAP devices we allow distributing healthcare at all levels, reducing the load on intensive care units, promoting treatment in the early stages at homecare. A significant part of the COVID-19 pneumonia patients requires not only an oxygen supply but also additional air pressure.

Laser speckle imaging for early detection of microbial colony forming units.

In this study, an optical contactless laser speckle imaging technique for the early identification of bacterial colony-forming units was tested. The aim of this work is to compare the laser speckle imaging method for the early assessment of microbial activity with standard visual inspection under white light illumination. In presented research, the growth of bacterial colonies on the solid medium was observed and analyzed.

Autofluorescence imaging for recurrence detection in skin cancer postoperative scars.

This clinical study is a first attempt to use autofluorescence for recurrence diagnosis of skin cancer in postoperative scars. The proposed diagnostic parameter is based on a reduction in scar autofluorescence, evaluated in the green spectral channel. The validity of the method has been tested on 110 postoperative scars from 56 patients suspected of non-melanoma skin cancer, with eight patients (13 scars) available for the repeated examination.