BACKGROUND AND PURPOSE: MRA is widely accepted as a noninvasive diagnostic tool for the detection of intracranial aneurysms, but detection is still a challenging task with rather low detection rates. Our aim was to examine the performance of a computer-aided diagnosis algorithm for detecting intracranial aneurysms on MRA in a clinical setting. MATERIALS AND METHODS: Aneurysm detectability was evaluated retrospectively in 48 subjects with and without computer-aided diagnosis by 6 readers using a clinical 3D viewing system. Aneurysms ranged from 1.1 to 6.0 mm (mean = 3.12 mm, median = 2.50 mm). We conducted a multireader, multicase, double-crossover design, free-response, observer-performance study on sets of images from different MRA scanners by using DSA as the reference standard. Jackknife alternative free-response operating characteristic curve analysis with the figure of merit was used. RESULTS: For all readers combined, the mean figure of merit improved from 0.655 to 0.759, indicating a change in the figure of merit attributable to computer-aided diagnosis of 0.10 (95% CI, 0.03-0.18), which was statistically significant (F(1,47) = 7.00, P = .011). Five of the 6 radiologists had improved performance with computer-aided diagnosis, primarily due to increased sensitivity. CONCLUSIONS: In conditions similar to clinical practice, using computer-aided diagnosis significantly improved radiologists' detection of intracranial DSA-confirmed aneurysms of ≤6 mm.

• MeSH
• Algorithms * MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Intracranial Aneurysm diagnostic imaging   MeSH
• Humans MeSH
• Magnetic Resonance Angiography methods   MeSH
• Radiography MeSH
• Retrospective Studies MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

• Keywords
• computer-aided sperm analysis, small-object detection, sperm-cell detection, yolo,
• MeSH
• Semen Analysis MeSH
• Humans MeSH
• Sperm Motility MeSH
• Infertility, Male * diagnosis   MeSH
• Semen * MeSH
• Spermatozoa MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Review MeSH

BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems. METHODS: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network. RESULTS: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset. CONCLUSIONS: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

• Keywords
• CAD, CNN, EEG, Seizures,
• MeSH
• Diagnosis, Computer-Assisted MeSH
• Electroencephalography methods   MeSH
• Humans MeSH
• Neural Networks, Computer * MeSH
• Signal Processing, Computer-Assisted MeSH
• Systems Analysis MeSH
• Seizures * diagnostic imaging   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

PURPOSE: Breast ultrasonography (US) presents an alternative to mammography in young asymptomatic individuals and a complementary examination in screening of women with dense breasts. Handheld US is the standard-of-care, yet when used in whole-breast examination, no effort has been devoted to monitoring breast coverage and missed regions, which is the purpose of this study. METHODS: We introduce a computer-aided system assisting radiologists and US technologists in covering the whole breast with minimum alteration to the standard workflow. The proposed system comprises a standard US device, proprietary electromagnetic 3D tracking technology and software that combines US visual and tracking data to estimate a probe trajectory, total time spent in different breast segments, and a map of missed regions. A case study, which involved four radiologists (two junior and two senior) performing whole-breast ultrasound in 75 asymptomatic patients, was conducted to test the importance and relevance of the system. RESULTS: The mean process time per breast was [Formula: see text], with no statistically significant difference between the left and the right sides, and slightly longer examination time of junior radiologists. The process time density shows that central parts of the breast have better coverage compared to the periphery. Within the central part, missed regions of minimum detectable size of [Formula: see text] occur in [Formula: see text] of examinations, and non-negligible [Formula: see text] regions occur in [Formula: see text] of cases. CONCLUSION: The results of the case study indicate that missed regions are present in handheld whole-breast US, which renders the proposed system for tracking the probe position during examination a valuable tool for monitoring coverage.

• Keywords
• Breast, Cancer, Coverage, Screening, Tracking, Ultrasound,
• MeSH
• Equipment Design MeSH
• Diagnosis, Computer-Assisted * MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Mammography methods   MeSH
• Breast Neoplasms diagnostic imaging   MeSH
• Computers, Handheld MeSH
• Computer Systems MeSH
• Image Processing, Computer-Assisted MeSH
• Breast diagnostic imaging   MeSH
• Reproducibility of Results MeSH
• Software MeSH
• Ultrasonography, Mammary methods   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

A number of studies have reported a significant improvement of the postoperative alignment, when computer-navigated total knee arthroplasty (TKA) was compared with conventional techniques. However, no studies are available on the functional and patient-relevant outcomes after computer-assisted knee replacement. In a prospective, randomized trial comparing 27 computer-assisted TKAs with 25 conventional implantations, the Knee Society Score was used to assess functional status, and the WOMAC questionnaire was used to record the disease-specific, patient-relevant outcome. At a twelve-month follow-up no significant difference was detected between the two patient groups in either the scores or the number of complications and range of postoperative knee flexion. The results are in agreement with those reported in other studies on the effect of conventional TKA. With the patient group of this size it can be concluded that computer-navigated TKA gives short-term resuits comparable with those achieved by conventional methods of implantation.

• MeSH
• Surgery, Computer-Assisted * MeSH
• Surgical Wound Infection MeSH
• Middle Aged MeSH
• Humans MeSH
• Range of Motion, Articular MeSH
• Aged MeSH
• Arthroplasty, Replacement, Knee * methods   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Randomized Controlled Trial MeSH

Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.

• Keywords
• Anomaly detection, Attention mechanism, Deep convolutional neural network, Localization, Wireless capsule endoscopy,
• MeSH
• Algorithms MeSH
• Capsule Endoscopy * MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted MeSH
• ROC Curve MeSH
• Machine Learning MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

• Keywords
• DNN, RNN, SVM, breast, cancer, deep learning, detection, neural network, visual techniques,
• MeSH
• Infrared Rays * MeSH
• Humans MeSH
• Breast Neoplasms diagnostic imaging   pathology   MeSH
• Image Processing, Computer-Assisted MeSH
• Breast diagnostic imaging   pathology   MeSH
• Sensitivity and Specificity MeSH
• Machine Learning * MeSH
• Thermography * MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

In recent years healthcare is undergoing significant changes due to technological innovations, with Artificial Intelligence (AI) being a key trend. Particularly in radiodiagnostics, according to studies, AI has the potential to enhance accuracy and efficiency. We focus on AI's role in diagnosing pulmonary lesions, which could indicate lung cancer, based on chest X-rays. Despite lower sensitivity in comparison to other methods like chest CT, due to its routine use, X-rays often provide the first detection of lung lesions. We present our deep learning-based solution aimed at improving lung lesion detection, especially during early-stage of illness. We then share results from our previous studies validating this model in two different clinical settings: a general hospital with low prevalence findings and a specialized oncology center. Based on a quantitative comparison with the conclusions of radiologists of different levels of experience, our model achieves high sensitivity, but lower specificity than comparing radiologists. In the context of clinical requirements and AI-assisted diagnostics, the experience and clinical reasoning of the doctor play a crucial role, therefore we currently lean more towards models with higher sensitivity over specificity. Even unlikely suspicions are presented to the doctor. Based on these results, it can be expected that in the future artificial intelligence will play a key role in the field of radiology as a supporting tool for evaluating specialists. To achieve this, it is necessary to solve not only technical but also medical and regulatory aspects. It is crucial to have access to quality and reliable information not only about the benefits but also about the limitations of machine learning and AI in medicine.

• Keywords
• artificial intelligence, deep learning, computer-aided diagnosis, chest X-ray, lung cancer, solitary pulmonary nodules, pulmonary masses,
• MeSH
• Early Detection of Cancer methods   MeSH
• Deep Learning MeSH
• Humans MeSH
• Lung Neoplasms * diagnostic imaging   MeSH
• Radiography, Thoracic * MeSH
• Retrospective Studies MeSH
• Sensitivity and Specificity MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic MeSH

Chest X-ray (CXR) is considered to be the most widely used modality for detecting and monitoring various thoracic findings, including lung carcinoma and other pulmonary lesions. However, X-ray imaging shows particular limitations when detecting primary and secondary tumors and is prone to reading errors due to limited resolution and disagreement between radiologists. To address these issues, we developed a deep-learning-based automatic detection algorithm (DLAD) to automatically detect and localize suspicious lesions on CXRs. Five radiologists were invited to retrospectively evaluate 300 CXR images from a specialized oncology center, and the performance of individual radiologists was subsequently compared with that of DLAD. The proposed DLAD achieved significantly higher sensitivity (0.910 (0.854-0.966)) than that of all assessed radiologists (RAD 10.290 (0.201-0.379), p < 0.001, RAD 20.450 (0.352-0.548), p < 0.001, RAD 30.670 (0.578-0.762), p < 0.001, RAD 40.810 (0.733-0.887), p = 0.025, RAD 50.700 (0.610-0.790), p < 0.001). The DLAD specificity (0.775 (0.717-0.833)) was significantly lower than for all assessed radiologists (RAD 11.000 (0.984-1.000), p < 0.001, RAD 20.970 (0.946-1.000), p < 0.001, RAD 30.980 (0.961-1.000), p < 0.001, RAD 40.975 (0.953-0.997), p < 0.001, RAD 50.995 (0.985-1.000), p < 0.001). The study results demonstrate that the proposed DLAD could be utilized as a decision-support system to reduce radiologists' false negative rate.

• Keywords
• YOLO, computer-aided diagnosis, convolutional neural network, deep learning, lung cancer, object detection, pulmonary lesion,
• Publication type
• Journal Article MeSH

Massively parallel spectroscopy (MPS) of many single nanoparticles in an aqueous dispersion is reported. As a model system, bioconjugated photon-upconversion nanoparticles (UCNPs) with a near-infrared excitation are prepared. The UCNPs are doped either with Tm3+ (emission 450 and 802 nm) or Er3+ (emission 554 and 660 nm). These UCNPs are conjugated to biotinylated bovine serum albumin (Tm3+-doped) or streptavidin (Er3+-doped). MPS is correlated with an ensemble spectra measurement, and the limit of detection (1.6 fmol L-1) and the linearity range (4.8 fmol L-1 to 40 pmol L-1) for bioconjugated UCNPs are estimated. MPS is used for observing the bioaffinity clustering of bioconjugated UCNPs. This observation is correlated with a native electrophoresis and bioaffinity assay on a microtiter plate. A competitive MPS bioaffinity assay for biotin is developed and characterized with a limit of detection of 6.6 nmol L-1. MPS from complex biological matrices (cell cultivation medium) is performed without increasing background. The compatibility with polydimethylsiloxane microfluidics is proven by recording MPS from a 30 μm deep microfluidic channel.

• MeSH
• Nanoparticles * chemistry   MeSH
• Spectrum Analysis MeSH
• Streptavidin MeSH
• Artificial Intelligence * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Streptavidin MeSH