Reinforced endotracheal tubes (ETTs) are designed to resist kinking or compression. However, these have a potential risk of being obstructed or severed by a patient's bite. We report a case in which a reinforced ETT was severed by tube-bite while the patient was in the prone position during an intensive care unit stay. Bronchoscopic evaluation showed that the severed distal part of the tube had lodged in the patient's right main bronchus, and it had to be surgically removed. The patency of reinforced ETTs should be carefully monitored in patients intubated in the prone position.
Airway Obstruction ; Bronchi* ; Bronchoscopy ; Humans ; Intensive Care Units ; Intubation ; Patient Rights ; Prone Position

In this study, we analyzed the healthcare system of North Korea using Kleczkowski's model which categorizes national healthcare infrastructure into five components: health resources, organization, healthcare delivery system, economic support, and management. It was found that the healthcare system in North Korea, which provides clinical medicine, Koryo medicine and preventive medicine, is constituted of a physician system of independent geographic sectors and centralized delivery system while maintaining free universal health coverage. These systems are all managed by the government and the People's Committee Party. However, North Korea has been confronted with severe economic difficulties since the 1990s, such that we question whether the healthcare system has been able to function properly despite of the lack of resources in general throughout the country.
Clinical Medicine ; Collodion ; Delivery of Health Care ; Democratic People's Republic of Korea ; Health Resources ; Preventive Medicine

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which weredetermined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The timerequired to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually,compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculatethese measurements, the efficiency of diagnosis and treatment may be improved.

With the progress of image-guided localization, body immobilization system, and computerized delivery of intensity-modulated radiation delivery, it became possible to perform spine radiosurgery. The next question is how to translate the high technology treatment to the clinical application. Clinical trials have been performed to demonstrate the feasibility of spine radiosurgery and efficacy of the treatment in the setting of spine metastasis, leading to the randomized trials by a cooperative group. Radiosurgery has also demonstrated its efficacy to decompress the spinal cord compression in selected group of patients. The experience indicates that spine radiosurgery has a potential to change the clinical practice in the management of spine metastasis and spinal cord compression.
Humans ; Immobilization ; Neoplasm Metastasis* ; Radiosurgery* ; Spinal Cord Compression* ; Spine*

A 52-year-old man presented with a brown to black nodule on his left cheek. Histological examination revealed nests of nevus cells throughout the upper and mid-dermis. An eosinophilic oval-shaped bony structure was found within the nevus. We report here on a case of nevus of Nanta combined with metastatic renal cell carcinoma.
Carcinoma, Renal Cell ; Cheek ; Eosinophils ; Humans ; Middle Aged ; Nevus

Endometriosis is a chronic inflammatory condition that affects fertility and could be toxic to the ovary. Endometrioma per se and surgical interventions for endometrioma significantly reduce the ovarian reserve. Therefore, to prepare for surgical intervention for endometrioma, the high-risk group with decreased ovarian reserve must be considered. There is no evidence to support the use of surgical intervention before in vitro fertilization (IVF) to improve the reproductive outcomes of subsequent IVF in infertile women with advanced-stage endometriosis or endometrioma. As surgical treatment has few benefits, IVF could be recommended immediately for aiding conception in these women. However, the reproductive prognosis of IVF may be worse in the more advanced stages of endometriosis. When dysmenorrhea is severe or when cancer is suspected, surgery prior to IVF may be necessary and justified. When the size of the endometrioma is very large, surgery could be required prior to IVF to facilitate access to follicles during oocyte retrieval or to improve the ovarian response to controlled ovarian stimulation. Prolonged pituitary downregulation in women with surgically diagnosed endometriosis may be helpful to increase the clinical pregnancy rate in subsequent IVF cycles. The purpose of this paper was to review the efficiency and clinical application of the surgical intervention and IVF for infertile women with advanced-stage endometriosis or endometrioma.
Cystectomy ; Down-Regulation ; Dysmenorrhea ; Endometriosis* ; Female ; Fertility ; Fertilization ; Fertilization in Vitro ; Humans ; Infertility ; Laparoscopy ; Oocyte Retrieval ; Ovarian Reserve ; Ovary ; Ovulation Induction ; Pregnancy Rate ; Prognosis

Reinforced endotracheal tubes (ETTs) are designed to resist kinking or compression. However, these have a potential risk of being obstructed or severed by a patient's bite. We report a case in which a reinforced ETT was severed by tube-bite while the patient was in the prone position during an intensive care unit stay. Bronchoscopic evaluation showed that the severed distal part of the tube had lodged in the patient's right main bronchus, and it had to be surgically removed. The patency of reinforced ETTs should be carefully monitored in patients intubated in the prone position.
Airway Obstruction ; Bronchi ; Bronchoscopy ; Humans ; Intensive Care Units ; Intubation ; Patient Rights ; Prone Position