OBJECTIVE: The purpose of this study is to compare landmark position between cephalometric radiography and midsagittal plane projected images from 3 dimensional (3D) CT. METHODS: Cephalometric radiographs and CT scans were taken from 20 patients for treatment of mandibular prognathism. After selection of landmarks, CT images were projected to the midsagittal plane and magnified to 110% according to the magnifying power of radiographs. These 2 images were superimposed with frontal and occipital bone. Common coordinate system was established on the base of FH plane. The coordinate value of each landmark was compared by paired t test and mean and standard deviation of difference was calculated. RESULTS: The difference was from -0.14 +/- 0.65 to -2.12 +/- 2.89 mm in X axis, from 0.34 +/- 0.78 to -2.36 +/- 2.55 mm (6.79 +/- 3.04 mm) in Y axis. There was no significant difference only 9 in X axis, and 7 in Y axis out of 20 landmarks. This might be caused by error from the difference of head positioning, by masking the subtle end structures, identification error from the superimposition and error from the different definition. CONCLUSIONS: This study revealed innate shortcomings of radiography. For the development of 3D cephalometry, more study was needed.
Axis, Cervical Vertebra ; Cephalometry ; Head ; Humans ; Masks ; Occipital Bone ; Prognathism

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology.

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology.

The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology.

This erratum is being published to correct the printing errors on page 267.

Three-dimensional (3D) printing has been particularly widely adopted in medical fields. Application of the 3D printing technique has even been extended to bio-cell printing for 3D tissue/organ development, the creation of scaffolds for tissue engineering, and actual clinical application for various medical parts. Of various medical fields, craniofacial plastic surgery is one of areas that pioneered the use of the 3D printing concept. Rapid prototype technology was introduced in the 1990s to medicine via computer-aided design, computer-aided manufacturing. To investigate the current status of 3D printing technology and its clinical application, a systematic review of the literature was conducted. In addition, the benefits and possibilities of the clinical application of 3D printing in craniofacial surgery are reviewed, based on personal experiences with more than 500 craniofacial cases conducted using 3D printing tactile prototype models.
Computer Simulation ; Computer-Aided Design ; Craniofacial Abnormalities ; Humans ; Orthognathic Surgery ; Surgery, Plastic* ; Surgical Flaps ; Tissue Engineering

MRI provides non-invasive and non-ionizing methods for the accurate anatomic depiction of the cardiovascular system. Based on the inherent flow sensitivity, MRI can be used to investigate hemodynamic features in patients with anatomical data within a single measurement. In particular, time-resolved and three-dimensional (3D) characterization of blood flow using 4D flow MRI has achieved considerable progress in recent years. The present article reviews the principle and procedures of 4D Flow MRI. Various fluid dynamic biomarkers for possible clinical usage are also described, including wall shear stress, turbulent kinetic energy, and relative pressure. Finally, this article provides an overview of the clinical applications of 4D Flow MRI in various cardiovascular regions.

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.
Artificial Intelligence* ; Brain* ; Computer Systems ; Diagnosis ; Humans ; Intelligence ; Machine Learning ; Patient Care ; Prognosis ; Stroke*

OBJECTIVE: Methamphetamine (MA) use rates in the United States (US) have consistently demonstrated geographical variation and have been higher in the West and Midwest. This uneven pattern of use could be explained by regional differences in MA manufacturing and distribution, but may also result from differences in altitude. The hypobaric hypoxia found at high altitude alters neurotransmitter synthesis in the brain, which may contribute to MA use. The present study investigated the relationship between mean altitude and MA use rate in the 48 contiguous US states and the District of Columbia. METHODS: State-level estimates of past year MA use were extracted from the National Survey on Drug Use and Health report. The mean altitude of each state was calculated using the Shuttle Radar Topography Mission altitude data set. RESULTS: There was a significant positive correlation between mean state altitude and MA use rate (r=0.66, p<0.0001). Multivariate linear regression analysis showed that altitude remained a significant predictor for MA use rate (beta=0.36, p=0.02), after adjusting for age, ethnicity, education, socioeconomic level, employment, MA laboratory incidents, subpopulations, and other substance use. CONCLUSION: Altitude appears to a possible contributing factor for regional variation of MA use in the US. Further studies will be required to determine biological changes in neurotransmission resulting from chronic mild hypoxia at high altitude in MA users.
Altitude* ; Anoxia ; Brain ; Dataset ; Education ; Employment ; Humans ; Linear Models ; Methamphetamine* ; Missions and Missionaries ; Neurotransmitter Agents ; Synaptic Transmission ; United States*

There have been several studies supporting a possible relationship between high suicide rate and high altitude. However socioeconomic status may confound this association because low socioeconomic status, which is known to be related to a high suicide rate, is also associated with living at high altitude. This study aims to explore whether the relationship between high altitude and high suicide rate remains after adjusting for socioeconomic status in South Korea. We collected demographic data of completed suicides, the mean altitude of the district where each suicide took place, and the mean income of each district. We analyzed the data using regression analysis before and after adjustment for mean income. We found that there is a positive correlation between altitude and suicide rate, even after adjustment for mean income. Thus, altitude appears to be an independent risk factor for suicide.
Altitude* ; Korea ; Risk Factors ; Social Class* ; Suicide*