Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists’ performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

PURPOSE: A method detecting change of jaw or alveolar bone density may be helpful in periodontal care, implant dentistry and evaluation of bone density of whole body. MATERIALS AND METHODS: In this study, bone density of intraoral periapical radiography using phantom-integrated XCP is compared with that of quantitative computed tomography (QCT). RESULTS: Bone density of intraoral periapical radiography and the one measured by QCT showed high correlation (correlation coefficient = 0.92, P<.001) in alveolar bone, and relatively high correlation (0.73, P<.001) in cancellous bone. CONCLUSION: This study revealed possibility of scoring of bone density by intraoral periapical radiography.
Bone Density* ; Dentistry ; Jaw ; Radiography*

PURPOSE: Status epilepticus (SE) is a neurological emergency disease because it can cause severe neurological complications. In order to avoid these complications, early diagnosis and appropriate treatment is required in SE. Febrile SE is the most common form of SE in children. We investigated the clinical characteristics and prognosis of patients with febrile SE is the first seizure of life. METHODS: We retrospectively reviewed the medical records of patients with SE as the first presentation of fever related seizures who visited our hospital from July 1996 to January 2013. Clinicodemographic characteristics, brain magnetic resonance imaging (MRI) and electro-encephalogram (EEG) findings, and anti-epileptic treatment were compared between two groups divided according to prognosis; fair vs. poor prognosis. RESULTS: Seventy-eight children were included in this study. The median age of the subjects was 20.0 months (interquartile range [IQR] 12.0–42.8). Fifty-one subjects had a fair prognosis, while twenty-seven subjects had a poor prognosis. Statistically significant differences was observed in the duration of seizure (P=0.043), the number of antiepileptic drugs (P<0.001) and the presence of abnormal EEG findings (P<0.001). CONCLUSION: Children with febrile SE as the first seizure of life are likely to reveal a poor prognosis in those whose seizure last longer or are controlled only through high step anti-epileptic drugs. Thus, in order to ensuring a better prognosis for such patients, appropriate treatment is needed to stop the seizure.
Anticonvulsants ; Brain ; Child* ; Early Diagnosis ; Electroencephalography ; Emergencies ; Epidemiologic Study Characteristics as Topic ; Fever* ; Humans ; Magnetic Resonance Imaging ; Medical Records ; Prognosis ; Retrospective Studies ; Seizures* ; Status Epilepticus*

Objectives: Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities. Methods: An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census. Results: The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were “very positive.” Conclusions This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.