Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.

To investigate the receptors mediating the regulation of norepinephrine (NE) release in human cerebral cortex slices, we examined the effects of opioid agonists for mu-, delta-, and kappa -receptors on the high potassium (15 mM) -evoked release of [3H]NE. [3H]NE release induced by high potassium was calcium-dependent and tetrodotoxin-sensitive. [D-Pen2, D-Pen5]enkephalin (DPDPE) and deltorphin II (Delt II) inhibited the stimulated release of norepinephrine in a dose-dependent manner. However, Tyr-D-Ala-Gly- (Me) Phe-Gly-ol and U69, 593 did not influence the NE release. Inhibitory effect of DPDPE and Delt-II was antagonized by naloxone, naltrindole, 7-benzylidenaltrexone and naltriben. These results suggest that both delta 1 and delta 2 receptors are involved in regulation of NE release in human cerebral cortex.
Analgesics, Opioid* ; Cerebral Cortex* ; Enkephalin, D-Penicillamine (2,5)- ; Humans* ; Naloxone ; Negotiating ; Norepinephrine ; Potassium ; Receptors, Opioid

OBJECTIVE: To investigate medical comorbidities and needs for medical and rehabilitation services of adults with cerebral palsy (CP) in Korea. METHODS: This was a prospective cross-sectional study. One hundred fifty-four adults with CP were enrolled in the study between February 2014 and December 2014. Information was obtained from participants regarding functional status, demographic and socioeconomic data, medical problems, and requirements for and utilization of medical and rehabilitation services. RESULTS: The participants included 93 males and 61 females with a mean age of 40.18±9.15 years. The medical check-up rate of adults with CP was lower than that of healthy adults and the total population with disabilities (53.2% vs. 58.6% vs. 70.4%). A quarter of the subjects failed to visit the hospital during the past year, and the main reason was the financial burden. Due to a cost burden and lack of knowledge, more than one-third of the subjects had unmet needs for rehabilitation services; the majority reported needs for rehabilitation services, such as physical therapy for pain management. CONCLUSION: The medical check-up rate was lower in the adults with CP, even though their medical comorbidities were not less than those of healthy people. Several non-medical reasons hindered them from receiving proper medical and rehabilitation services. Such barriers should be managed effectively.
Adult* ; Cerebral Palsy* ; Comorbidity ; Cross-Sectional Studies ; Female ; Humans ; Korea* ; Male ; Pain Management ; Prospective Studies ; Rehabilitation*

Intraoperative monitoring (IOM) utilizes electrophysiological techniques as a surrogate test and evaluation of nervous function while a patient is under general anesthesia. They are increasingly used for procedures, both surgical and endovascular, to avoid injury during an operation, examine neurological tissue to guide the surgery, or to test electrophysiological function to allow for more complete resection or corrections. The application of IOM during pediatric brain tumor resections encompasses a unique set of technical issues. First, obtaining stable and reliable responses in children of different ages requires detailed understanding of normal ageadjusted brain-spine development. Neurophysiology, anatomy, and anthropometry of children are different from those of adults. Second, monitoring of the brain may include risk to eloquent functions and cranial nerve functions that are difficult with the usual neurophysiological techniques. Third, interpretation of signal change requires unique sets of normative values specific for children of that age. Fourth, tumor resection involves multiple considerations including defining tumor type, size, location, pathophysiology that might require maximal removal of lesion or minimal intervention. IOM techniques can be divided into monitoring and mapping. Mapping involves identification of specific neural structures to avoid or minimize injury. Monitoring is continuous acquisition of neural signals to determine the integrity of the full longitudinal path of the neural system of interest. Motor evoked potentials and somatosensory evoked potentials are representative methodologies for monitoring. Free-running electromyography is also used to monitor irritation or damage to the motor nerves in the lower motor neuron level : cranial nerves, roots, and peripheral nerves. For the surgery of infratentorial tumors, in addition to free-running electromyography of the bulbar muscles, brainstem auditory evoked potentials or corticobulbar motor evoked potentials could be combined to prevent injury of the cranial nerves or nucleus. IOM for cerebral tumors can adopt direct cortical stimulation or direct subcortical stimulation to map the corticospinal pathways in the vicinity of lesion. IOM is a diagnostic as well as interventional tool for neurosurgery. To prove clinical evidence of it is not simple. Randomized controlled prospective studies may not be possible due to ethical reasons. However, prospective longitudinal studies confirming prognostic value of IOM are available. Furthermore, oncological outcome has also been shown to be superior in some brain tumors, with IOM. New methodologies of IOM are being developed and clinically applied. This review establishes a composite view of techniques used today, noting differences between adult and pediatric monitoring.
Adult ; Anesthesia, General ; Anthropometry ; Brain Neoplasms ; Brain ; Child ; Cranial Nerves ; Electromyography ; Evoked Potentials, Auditory, Brain Stem ; Evoked Potentials, Motor ; Evoked Potentials, Somatosensory ; Humans ; Infratentorial Neoplasms ; Intraoperative Neurophysiological Monitoring ; Longitudinal Studies ; Monitoring, Intraoperative ; Motor Neurons ; Muscles ; Neurophysiology ; Neurosurgery ; Peripheral Nerves ; Prospective Studies

Objective: Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs). Methods: EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1 ), second (T2 ), and third (T3 ) visits. The radiographic progression of the (n+1)th visit (Pn+1 =(mSASSSn+1 –mSASSSn )/(Tn+1 – Tn )≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn . We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation. Results: The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.