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.

Objective: The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods: One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results: The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.

Purpose: To investigate the prevalence of lower urinary tract symptoms/benign prostatic hyperplasia in a Korean population. Materials and Methods: The Korean Prostate & Voiding Health Association provided free prostate-related community health care and conducted surveys in all regions of Korea from 2001 to 2022 with the cooperation of local government public health centers. A total of 72,068 males older than 50 were surveyed and analyzed. History taking, International Prostate Symptom Score (IPSS), transrectal ultrasonography, prostate-specific antigen (PSA) testing, uroflowmetry, and urine volume testing were performed. Results: The mean prostate volumes in males in their 50s, 60s, 70s, and 80s or above were 24.7 g, 27.7 g, 31 g, and 33.7 g, respectively. The proportion of males with high PSA greater than 3 ng/mL was 3.8% among males in their 50s, 7.7% among males in their 60s, 13.1% among males in their 70s, and 17.9% among males 80 years of age or older. The mean IPSS total scores in males in their 50s, 60s, 70s, and 80s or above were 10.7, 12.7, 14.5, and 16, respectively. Severe symptoms were reported by 27.3% of males, whereas 51.7% reported moderate symptoms. The mean Qmax in males in their 50s, 60s, 70s, and 80s or above were 20 mL/s, 17.4 mL/s, 15.4 mL/s, and 13.8 mL/s, respectively. Conclusions In this population-based study, mean prostate volume, IPSS, PSA, and Qmax were 30.6±15.1 g, 14.8±8.2, 1.9±4.7 ng/mL, and 15.6±6.5 mL/s, respectively. Aging was significantly associated with increased prostate volume, PSA levels, and IPSS scores, and with decreased Qmax and urine volume.

Purpose: To investigate the prevalence of lower urinary tract symptoms/benign prostatic hyperplasia in a Korean population. Materials and Methods: The Korean Prostate & Voiding Health Association provided free prostate-related community health care and conducted surveys in all regions of Korea from 2001 to 2022 with the cooperation of local government public health centers. A total of 72,068 males older than 50 were surveyed and analyzed. History taking, International Prostate Symptom Score (IPSS), transrectal ultrasonography, prostate-specific antigen (PSA) testing, uroflowmetry, and urine volume testing were performed. Results: The mean prostate volumes in males in their 50s, 60s, 70s, and 80s or above were 24.7 g, 27.7 g, 31 g, and 33.7 g, respectively. The proportion of males with high PSA greater than 3 ng/mL was 3.8% among males in their 50s, 7.7% among males in their 60s, 13.1% among males in their 70s, and 17.9% among males 80 years of age or older. The mean IPSS total scores in males in their 50s, 60s, 70s, and 80s or above were 10.7, 12.7, 14.5, and 16, respectively. Severe symptoms were reported by 27.3% of males, whereas 51.7% reported moderate symptoms. The mean Qmax in males in their 50s, 60s, 70s, and 80s or above were 20 mL/s, 17.4 mL/s, 15.4 mL/s, and 13.8 mL/s, respectively. Conclusions In this population-based study, mean prostate volume, IPSS, PSA, and Qmax were 30.6±15.1 g, 14.8±8.2, 1.9±4.7 ng/mL, and 15.6±6.5 mL/s, respectively. Aging was significantly associated with increased prostate volume, PSA levels, and IPSS scores, and with decreased Qmax and urine volume.

Purpose: To investigate the prevalence of lower urinary tract symptoms/benign prostatic hyperplasia in a Korean population. Materials and Methods: The Korean Prostate & Voiding Health Association provided free prostate-related community health care and conducted surveys in all regions of Korea from 2001 to 2022 with the cooperation of local government public health centers. A total of 72,068 males older than 50 were surveyed and analyzed. History taking, International Prostate Symptom Score (IPSS), transrectal ultrasonography, prostate-specific antigen (PSA) testing, uroflowmetry, and urine volume testing were performed. Results: The mean prostate volumes in males in their 50s, 60s, 70s, and 80s or above were 24.7 g, 27.7 g, 31 g, and 33.7 g, respectively. The proportion of males with high PSA greater than 3 ng/mL was 3.8% among males in their 50s, 7.7% among males in their 60s, 13.1% among males in their 70s, and 17.9% among males 80 years of age or older. The mean IPSS total scores in males in their 50s, 60s, 70s, and 80s or above were 10.7, 12.7, 14.5, and 16, respectively. Severe symptoms were reported by 27.3% of males, whereas 51.7% reported moderate symptoms. The mean Qmax in males in their 50s, 60s, 70s, and 80s or above were 20 mL/s, 17.4 mL/s, 15.4 mL/s, and 13.8 mL/s, respectively. Conclusions In this population-based study, mean prostate volume, IPSS, PSA, and Qmax were 30.6±15.1 g, 14.8±8.2, 1.9±4.7 ng/mL, and 15.6±6.5 mL/s, respectively. Aging was significantly associated with increased prostate volume, PSA levels, and IPSS scores, and with decreased Qmax and urine volume.

Purpose: To investigate the prevalence of lower urinary tract symptoms/benign prostatic hyperplasia in a Korean population. Materials and Methods: The Korean Prostate & Voiding Health Association provided free prostate-related community health care and conducted surveys in all regions of Korea from 2001 to 2022 with the cooperation of local government public health centers. A total of 72,068 males older than 50 were surveyed and analyzed. History taking, International Prostate Symptom Score (IPSS), transrectal ultrasonography, prostate-specific antigen (PSA) testing, uroflowmetry, and urine volume testing were performed. Results: The mean prostate volumes in males in their 50s, 60s, 70s, and 80s or above were 24.7 g, 27.7 g, 31 g, and 33.7 g, respectively. The proportion of males with high PSA greater than 3 ng/mL was 3.8% among males in their 50s, 7.7% among males in their 60s, 13.1% among males in their 70s, and 17.9% among males 80 years of age or older. The mean IPSS total scores in males in their 50s, 60s, 70s, and 80s or above were 10.7, 12.7, 14.5, and 16, respectively. Severe symptoms were reported by 27.3% of males, whereas 51.7% reported moderate symptoms. The mean Qmax in males in their 50s, 60s, 70s, and 80s or above were 20 mL/s, 17.4 mL/s, 15.4 mL/s, and 13.8 mL/s, respectively. Conclusions In this population-based study, mean prostate volume, IPSS, PSA, and Qmax were 30.6±15.1 g, 14.8±8.2, 1.9±4.7 ng/mL, and 15.6±6.5 mL/s, respectively. Aging was significantly associated with increased prostate volume, PSA levels, and IPSS scores, and with decreased Qmax and urine volume.