Personal identification using a cross-sectional hyperspectral image of a hand.

Significance: I explore hyperspectral imaging, a rapid and noninvasive technique with significant potential in biometrics and medical diagnosis. Personal identification was performed using cross-sectional hyperspectral images of palms, offering a simpler and more robust method than conventional vascular pattern identification methods.

Aim: I aim to demonstrate the potential of local cross-sectional hyperspectral palm images to identify individuals with high accuracy.

Comparative analysis of GPT-4-based ChatGPT's diagnostic performance with radiologists using real-world radiology reports of brain tumors.

Objectives: Large language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists.

Data set terminology of deep learning in medicine: a historical review and recommendation.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field.

Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.

Purpose: To compare the diagnostic performance among Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT‑4 with vision (GPT-4V) based ChatGPT, and radiologists in challenging neuroradiology cases.

Methods: We collected 32 consecutive "Freiburg Neuropathology Case Conference" cases from the journal Clinical Neuroradiology between March 2016 and December 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated a diagnosis for each case.

Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.

Background: Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another.

Purpose: This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging.

Data Sources: PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images.

The effects of dark chocolate on cognitive performance during cognitively demanding tasks: A randomized, single-blinded, crossover, dose-comparison study.

Dark chocolate, rich in polyphenols, increases cerebral blood flow and improves cognitive function. This study aimed to determine whether the consumption of chocolate with a high concentration of polyphenols helps to maintain cognitive performance during cognitively demanding tasks. In this randomized, single-blinded, crossover, dose-comparison study, 18 middle-aged adults consumed two types of chocolate (25 g each), one with a high concentration (635.

Cacao Polyphenol-Rich Dark Chocolate Intake Contributes to Efficient Brain Activity during Cognitive Tasks: A Randomized, Single-Blinded, Crossover, and Dose-Comparison fMRI Study.

Cacao polyphenol-enriched dark chocolate may have beneficial effects on human health, such as facilitating maintaining good performance in long-lasting cognitive tasks. This study examined the effects of dark chocolate intake on improving brain function during cognitive tasks using functional magnetic resonance imaging (fMRI). In this randomized, single-blinded, crossover, and dose-comparison study, 26 healthy middle-aged participants ingested dark chocolate (25 g) either with a low concentration (LC) (211.

Morphomics via next-generation electron microscopy.

The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable.

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

Purpose: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology.

Methods: We collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023.

Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan.

Background: Chest radiographs are widely available and cost-effective; however, their usefulness as a biomarker of ageing using multi-institutional data remains underexplored. The aim of this study was to develop a biomarker of ageing from chest radiography and examine the correlation between the biomarker and diseases.

Methods: In this retrospective, multi-institutional study, we trained, tuned, and externally tested an artificial intelligence (AI) model to estimate the age of healthy individuals using chest radiographs as a biomarker.

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study.

Background Carbon 11 (C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set).

The Association of Metabolic Brain MRI, Amyloid PET, and Clinical Factors: A Study of Alzheimer's Disease and Normal Controls From the Open Access Series of Imaging Studies Dataset.

Background: Although brain activities in Alzheimer's disease (AD) might be evaluated MRI and PET, the relationships between brain temperature (BT), the index of diffusivity along the perivascular space (ALPS index), and amyloid deposition in the cerebral cortex are still unclear.

Purpose: To investigate the relationship between metabolic imaging measurements and clinical information in patients with AD and normal controls (NCs).

Study Type: Retrospective analysis of a prospective dataset.

Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study.

Background: Chest radiography is a common and widely available examination. Although cardiovascular structures-such as cardiac shadows and vessels-are visible on chest radiographs, the ability of these radiographs to estimate cardiac function and valvular disease is poorly understood. Using datasets from multiple institutions, we aimed to develop and validate a deep-learning model to simultaneously detect valvular disease and cardiac functions from chest radiographs.

The Association between Atherosclerotic Disease Risk Factors and Serum 25-Hydroxyvitamin D Concentration in Japanese Subjects.

Recent studies have described that vitamin D deficiency/insufficiency is associated with hypertension, insulin resistance, and dyslipidemia, which are major components of metabolic syndrome causing atherosclerosis. Therefore, we investigated the relationship between serum 25-hydroxyvitamin D [25(OH)D] concentration and atherosclerotic disease risk factors in healthy Japanese adults. In the present cross-sectional study, 1,177 subjects (348 males and 829 females) aged 20-72 y living in Japan (34.

Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis.

Background: Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.

Methods: A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed.

Deep learning-based screening tool for rotator cuff tears on shoulder radiography.

Background: Early diagnosis of rotator cuff tears is essential for appropriate and timely treatment. Although radiography is the most used technique in clinical practice, it is difficult to accurately rule out rotator cuff tears as an initial imaging diagnostic modality. Deep learning-based artificial intelligence has recently been applied in medicine, especially diagnostic imaging.

Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma.

We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence.