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General General

Biomedical optical fibers.

In Lab on a chip

Optical fibers with the ability to propagate and transfer data via optical signals have been used for decades in medicine. Biomaterials featuring the properties of softness, biocompatibility, and biodegradability enable the introduction of optical fibers' uses in biomedical engineering applications such as medical implants and health monitoring systems. Here, we review the emerging medical and health-field applications of optical fibers, illustrating the new wave for the fabrication of implantable devices, wearable sensors, and photodetection and therapy setups. A glimpse of fabrication methods is also provided, with the introduction of 3D printing as an emerging fabrication technology. The use of artificial intelligence for solving issues such as data analysis and outcome prediction is also discussed, paving the way for the new optical treatments for human health.

Rezapour Sarabi Misagh, Jiang Nan, Ozturk Ece, Yetisen Ali K, Tasoglu Savas


General General

Optimizing illumination for precise multi-parameter estimations in coherent diffractive imaging.

In Optics letters

Coherent diffractive imaging (CDI) is widely used to characterize structured samples from measurements of diffracting intensity patterns. We introduce a numerical framework to quantify the precision that can be achieved when estimating any given set of parameters characterizing the sample from measured data. The approach, based on the calculation of the Fisher information matrix, provides a clear benchmark to assess the performance of CDI methods. Moreover, by optimizing the Fisher information metric using deep learning optimization libraries, we demonstrate how to identify the optimal illumination scheme that minimizes the estimation error under specified experimental constraints. This work paves the way for an efficient characterization of structured samples at the sub-wavelength scale.

Bouchet Dorian, Seifert Jacob, Mosk Allard P


General General

Multi-scale retinal vessel segmentation using encoder-decoder network with squeeze-and-excitation connection and atrous spatial pyramid pooling.

In Applied optics

The segmentation of blood vessels in retinal images is crucial to the diagnosis of many diseases. We propose a deep learning method for vessel segmentation based on an encoder-decoder network combined with squeeze-and-excitation connection and atrous spatial pyramid pooling. In our implementation, the atrous spatial pyramid pooling allows the network to capture features at multiple scales, and the high-level semantic information is combined with low-level features through the encoder-decoder architecture to generate segmentations. Meanwhile, the squeeze-and-excitation connections in the proposed network can adaptively recalibrate features according to the relationship between different channels of features. The proposed network can achieve precise segmentation of retinal vessels without hand-crafted features or specific post-processing. The performance of our model is evaluated in terms of visual effects and quantitative evaluation metrics on two publicly available datasets of retinal images, the Digital Retinal Images for Vessel Extraction and Structured Analysis of the Retina datasets, with comparison to 12 representative methods. Furthermore, the proposed network is applied to vessel segmentation on local retinal images, which demonstrates promising application prospect in medical practices.

Xie Huiying, Tang Chen, Zhang Wei, Shen Yuxin, Lei Zhengkun


General General

A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : Low back pain (LBP) is an increasingly burdensome condition for patients and health professionals alike, with consistent demonstration of increasing persistent pain and disability. Previous decision support tools for LBP management have focused on a subset of factors owing to time constraints and ease of use for the clinician. With the explosion of interest in machine learning tools and the commitment from Western governments to introduce this technology, there are opportunities to develop intelligent decision support tools. We will do this for LBP using a Bayesian network, which will entail constructing a clinical reasoning model elicited from experts.

OBJECTIVE : This paper proposes a method for conducting a modified RAND appropriateness procedure to elicit the knowledge required to construct a Bayesian network from a group of domain experts in LBP, and reports the lessons learned from the internal pilot of the procedure.

METHODS : We propose to recruit expert clinicians with a special interest in LBP from across a range of medical specialties, such as orthopedics, rheumatology, and sports medicine. The procedure will consist of four stages. Stage 1 is an online elicitation of variables to be considered by the model, followed by a face-to-face workshop. Stage 2 is an online elicitation of the structure of the model, followed by a face-to-face workshop. Stage 3 consists of an online phase to elicit probabilities to populate the Bayesian network. Stage 4 is a rudimentary validation of the Bayesian network.

RESULTS : Ethical approval has been obtained from the Research Ethics Committee at Queen Mary University of London. An internal pilot of the procedure has been run with clinical colleagues from the research team. This showed that an alternating process of three remote activities and two in-person meetings was required to complete the elicitation without overburdening participants. Lessons learned have included the need for a bespoke online elicitation tool to run between face-to-face meetings and for careful operational definition of descriptive terms, even if widely clinically used. Further, tools are required to remotely deliver training about self-identification of various forms of cognitive bias and explain the underlying principles of a Bayesian network. The use of the internal pilot was recognized as being a methodological necessity.

CONCLUSIONS : We have proposed a method to construct Bayesian networks that are representative of expert clinical reasoning for a musculoskeletal condition in this case. We have tested the method with an internal pilot to refine the process prior to deployment, which indicates the process can be successful. The internal pilot has also revealed the software support requirements for the elicitation process to model clinical reasoning for a range of conditions.


Hill Adele, Joyner Christopher H, Keith-Jopp Chloe, Yet Barbaros, Tuncer Sakar Ceren, Marsh William, Morrissey Dylan


Bayesian methods, back pain, consensus, decision making

General General

Classification of malignant lung cancer using deep learning.

In Journal of medical engineering & technology

In the automatic detection of suspicious shaded regions on CT images derived from the LIDC-IDRI dataset, the diagnostic system plays a significant role. This paper introduces an automatic recognition method for lung nodules of the regions of concern (ROI). The lung regions are segmented from DICOM image size 512 × 512 by adding a median filter, Gaussian filter, Gabor filter and watershed algorithm. AlexNet uses 227 × 227 × 3 with "fc7" (fully connected) layers and GoogLeNet uses 224 × 224 × 3 with "pool5-drop 7 × 7 s1" layers. Here, the authors explain what is better about AlexNet and GoogLeNet through its performance analysis, feature extraction, classification, sensitivity, specificity, detection and false alarm rate with time complexity. A multi-class SVM classifier with 100% precision and specificity provided the best performance in deep learning neural networks.

Kumar Vinod, Bakariya Brijesh


AlexNet, Gabor filter, GoogLeNet, mSVM, nodule detection

General General

Application of Artificial Intelligence for Medical Research.

In Biomolecules

The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [...].

Hamamoto Ryuji