Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology

Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence’s impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.

Three-dimensional (3D) velocity map imaging: from technique to application

The velocity map imaging (VMI) technique was first introduced by Eppink and Parker in 1997, as an improvement to the original ion imaging method by Houston and Chandler in 1987. The method has gained huge popularity over the past two decades and has become a standard tool for measuring high-resolution translational energy and angular distributions of ions and electrons. VMI has evolved gradually from 2D momentum measurements to 3D measurements with various implementations and configurations. The most recent advancement has brought unprecedented 3D performance to the technique in terms of resolutions (both spatial and temporal), multi-hit capability as well as acquisition speed while maintaining many attractive attributes afforded by conventional VMI such as being simple, cost-effective, visually appealing and versatile. In this tutorial we will discuss many technical aspects of the recent advancement and its application in probing correlated chemical dynamics.

Liver Transplant Imaging prior to and during the COVID-19 Pandemic

Background. The suspension of the surgical activity, the burden of the infection in immunosuppressed patients, and the comorbidities underlying end-stage organ disease have impacted transplant programs significantly, even life-saving procedures, such as liver transplantation. Methods. A review of the literature was conducted to explore the challenges faced by transplant programs and the adopted strategies to overcome them, with a focus on indications for imaging in liver transplant candidates. Results. Liver transplantation relies on an appropriate imaging method for its success. During the Coronavirus Disease 2019 (COVID-19) pandemic, chest CT showed an additional value to detect early signs of SARS-CoV-2 infection and other screening modalities are less accurate than radiology. Conclusion. There is an emerging recognition of the chest CT value to recommend its use and help COVID-19 detection in patients. This examination appears highly sensitive for liver transplant candidates and recipients, who otherwise would have not undergone it, particularly when asymptomatic.

Identification of ∆9-tetrahydrocannabinol (THC) impairment using functional brain imaging

The primary cannabinoid in cannabis, Δ9-tetrahydrocannabinol (THC), causes intoxication and impaired function, with implications for traffic, workplace, and other situational safety risks. There are currently no evidence-based methods to detect cannabis-impaired driving, and current field sobriety tests with gold-standard, drug recognition evaluations are resource-intensive and may be prone to bias. This study evaluated the capability of a simple, portable imaging method to accurately detect individuals with THC impairment. In this double-blind, randomized, cross-over study, 169 cannabis users, aged 18–55 years, underwent functional near-infrared spectroscopy (fNIRS) before and after receiving oral THC and placebo, at study visits one week apart. Impairment was defined by convergent classification by consensus clinical ratings and an algorithm based on post-dose tachycardia and self-rated “high.” Our primary outcome, PFC oxygenated hemoglobin concentration (HbO), was increased after THC only in participants operationalized as impaired, independent of THC dose. ML models using fNIRS time course features and connectivity matrices identified impairment with 76.4% accuracy, 69.8% positive predictive value (PPV), and 10% false-positive rate using convergent classification as ground truth, which exceeded Drug Recognition Evaluator-conducted expanded field sobriety examination (67.8% accuracy, 35.4% PPV, and 35.4% false-positive rate). These findings demonstrate that PFC response activation patterns and connectivity produce a neural signature of impairment, and that PFC signal, measured with fNIRS, can be used as a sole input to ML models to objectively determine impairment from THC intoxication at the individual level. Future work is warranted to determine the specificity of this classifier to acute THC impairment.ClinicalTrials.gov Identifier: NCT03655717

Single Particle Hopping as an Indicator for Evaluating Electrocatalysts

Design and screening electrocatalysts for gas evolution reactions suffer from scanty understanding of multi-phase processes at the electrode-electrolyte interface. Due to the complexity of multi-phase interface, it is still a great challenge to capture gas evolution dynamics under operando condition to precisely portray the intrinsic catalytic performance of interface. Here, we establish a single particle imaging method to real time monitor a potential-dependent vertical motion or hopping of electrocatalysts induced by electrogenerated gas nanobubbles. The hopping feature of single particle is closely correlated with intrinsic activities of electrocatalysts, thus is developed to be an indicator to evaluate gas evolution performance of various electrocatalysts. This optical indicator diminishes interferences from heterogeneous morphologies, non-Faradaic processes and parasitic side reactions that are unavoidable in conventional electrochemical measurements, therefore enables precise evaluation and high-throughput screening of catalysts for gas evolution systems.

Testicular metastasis in prostate cancer: A rare case of testicular metastasis diagnosed with 68Ga-PSMA and review of the literature

Objective: Testicular metastasis in prostate cancer is a rare entity. We aimed to report the case where this rare condition was diagnosed with Gallium prostate-specific membrane antigen–positron emission tomography/computed tomography (68Ga-PSMA-PET/CT). Subjects/patients and methods: A 68-year-old male with a prostate adenocarcinoma presented with testicular metastasis. The patient was diagnosed with 68Ga-PSMA-PET/CT, and bilateral inguinal orchiectomy was performed. Herein, our case is presented, and a short review of the literature is carried out. Conclusion: 68Ga-PSMA-PET/CT is an effective imaging method for detecting rare metastases. Level of evidence: 4

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

Dual-energy CT: minimal essentials for radiologists

Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.