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Purpose@#Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation. @*Methods@#The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model’s performance was evaluated using accuracy, precision, and recall as metrics. @*Results@#The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model. @*Conclusions@#The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.
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Purpose@#In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach. @*Methods@#We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed). @*Results@#As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery. @*Conclusions@#The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.
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Artificial intelligence (AI) is used in various fields of medicine, with applications encompassing all areas of medical services, such as the development of medical robots, the diagnosis and personalized treatment of diseases, and personalized healthcare. Medical AI research and development have been largely focused on diagnosis, prediction, treatment, and management as an auxiliary means of patient care. AI is mainly used in the fields of personal healthcare and diagnostic imaging. In urology, substantial investments are being made in the development of urination monitoring systems in the personal healthcare field and diagnostic solutions for ureteral stricture and urolithiasis in the diagnostic imaging field. This paper describes AI applications for urinary diseases and discusses current trends and future perspectives in AI research.
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Purpose@#This paper proposes a technological system that uses artificial intelligence to recognize and guide the operator to the exact stenosis area during endoscopic surgery in patients with urethral or ureteral strictures. The aim of this technological solution was to increase surgical efficiency. @*Methods@#The proposed system utilizes the ResNet-50 algorithm, an artificial intelligence technology, and analyzes images entering the endoscope during surgery to detect the stenosis location accurately and provide intraoperative clinical assistance. The ResNet-50 algorithm was chosen to facilitate accurate detection of the stenosis site. @*Results@#The high recognition accuracy of the system was confirmed by an average final sensitivity value of 0.96. Since sensitivity is a measure of the probability of a true-positive test, this finding confirms that the system provided accurate guidance to the stenosis area when used for support in actual surgery. @*Conclusions@#The proposed method supports surgery for patients with urethral or ureteral strictures by applying the ResNet-50 algorithm. The system analyzes images entering the endoscope during surgery and accurately detects stenosis, thereby assisting in surgery. In future research, we intend to provide both conservative and flexible boundaries of the strictures.
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Purpose@#There are various neurogenic bladder patterns that occur in patients during stroke. Among these patterns, the focus was mainly on the patient’s facial parsy diagnosis. Stroke requires early response, and it is most important to identify initial symptoms such as facial parsy. There is an urgent need for a diagnostic technology that notifies patients and caregivers of the onset of disease in the early stages of stroke. We developed an artificial intelligence (AI) stroke early-stage analysis software that can alert the early stage of stroke through analysis of facial muscle abnormalities for the elderly neurogenic bladder prevention. @*Methods@#The method proposed in this paper developed a learning-based deep learning analysis technology that outputs the initial stage of stroke after acquiring a high-definition digital image and then deep learning face analysis. The applied AI model was applied as a multimodal deep learning concept. The system is linked and integrated with the existing urine management integrated system to support patient management with a total-care concept. @*Results@#We developed an AI stroke early-stage analysis software that can alert the early stage of stroke with 86% hit performance through analysis of facial muscle abnormalities in the elderly. This result shows the validation result of the landmark image learning model based on the distance learning model. @*Conclusions@#We developed an AI stroke early-stage diagnostic system as a wellness personal medical service plan and prevent cases of missing golden time when existing stroke occurs. In order to secure and facilitate distribution of this, it was developed in the form of AI analysis software so that it can be mounted on various hardware products. In the end, it was found that using AI for these stroke diagnoses and making them quickly and accurately had a positive effect indirectly, if not directly, on the neurogenic bladder.
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Purpose@#In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. @*Methods@#We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. @*Results@#Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. @*Conclusions@#This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.
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Purpose@#In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. @*Methods@#We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. @*Results@#Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. @*Conclusions@#This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.
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PURPOSE: Though it is very important obtaining exact data about patients’ voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients’ wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management. METHODS: We designed a device capable of recognizing urination time and intervals based on specific postures of the patient and consistent changes in posture. These motion data were obtained by a smart band worn on the wrist. An algorithm that recognizes the repetitive and common 3-step behavior for urination (forward movement, urination, backward movement) was devised based on the movement and tilt angle data collected from a 3-axis accelerometer. The sequence of body movements during voiding has consistent temporal characteristics, so we used a recurrent neural network and long short-term memory based framework to analyze the sequential data and to recognize urination time. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. A comparative study was conducted between real voiding and device-detected voiding to assess the performance of the proposed recognition technology. RESULTS: The accuracy of the algorithm was calculated based on clinical guidelines established by urologists. The accuracy of this detecting device was high (up to 94.2%), proving the robustness of the proposed algorithm. CONCLUSIONS: This urination behavior recognition technology showed high accuracy and could be applied in clinical settings to characterize patients’ voiding patterns. As wearable devices are developed and generalized, algorithms detecting consistent sequential body movement patterns reflecting specific physiologic behavior might be a new methodology for studying human physiologic behavior.
Subject(s)
Humans , Memory, Short-Term , Posture , Urination , WristABSTRACT
PURPOSE: We compared the efficacy of tamsulosin between 0.2 mg and 0.4 mg in Asian prostatic hyperplasia (BPH) patients using network meta-analysis due to lack of studies with direct comparison. METHODS: The literature search was conducted using the MEDLINE, Embase, and Cochrane Library. Keywords used were “BPH,”“tamsulosin,”“placebo.” Experimental groups were defined as tamsulosin 0.2 mg (Tam 0.2) and 0.4 mg (Tam 0.4) and common control group was defined as placebo for indirect treatment comparison. Mixed treatment comparison was performed including one direct comparison study. RESULTS: Seven studies met the eligible criteria. Indirect treatment comparison revealed that total International Prostate Symptoms Score (IPSS) and quality of life score of IPSS were not significantly different in Tam 0.2 and Tam 0.4 (P>0.05). There was no significant difference of maximal flow rate and postvoid residual urine volume in Tam 0.2 and Tam 0.4 (P>0.05). Mixed treatment comparison including one direct comparison study showed inconsistency (P<0.001). Therefore, analysis using direct treatment comparison effect sizes of Tam 0.2 vs. placebo and Tam 0.4 vs. placebo was done and there was no significant difference. CONCLUSIONS: Network meta-analysis showed no difference of efficacy between tamsulosin 0.2 mg and 0.4 mg and the evidence of tamsulosin 0.4 mg as initial dose for Asian BPH patient seems to be insufficient. Therefore, initial dose of tamsulosin for Asian BPH patient should be 0.2 mg.
Subject(s)
Humans , Male , Asian People , Prostate , Prostatic Hyperplasia , Quality of LifeABSTRACT
PURPOSE: This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. METHODS: This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. RESULTS: An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. CONCLUSIONS: The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.
Subject(s)
Humans , Acceleration , Arm , Posture , Urination , VibrationABSTRACT
The first author's affiliation should be corrected.
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Recent developments in virtual, augmented, and mixed reality have introduced a considerable number of new devices into the consumer market. This momentum is also affecting the medical and health care sector. Although many of the theoretical and practical foundations of virtual reality (VR) were already researched and experienced in the 1980s, the vastly improved features of displays, sensors, interactivity, and computing power currently available in devices offer a new field of applications to the medical sector and also to urology in particular. The purpose of this review article is to review the extent to which VR technology has already influenced certain aspects of medicine, the applications that are currently in use in urology, and the future development trends that could be expected.
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Biofeedback, Psychology , Foundations , Health Care Sector , UrologyABSTRACT
OBJECTIVE: There was a recent study to explore the cerebral regions associated with sexual arousal in depressed women using functional magnetic resonance imaging (fMRI). The purpose of this neuroimaging study was to investigate the effects of antidepressant treatment on sexual arousal in depressed women. METHODS: Seven depressed women with sexual arousal dysfunction (mean age: 41.7+/-13.8, mean scores of the Beck Depression Inventory (BDI) and the 17-item Hamilton Rating Scale for Depression (HAMD-17): 35.6+/-7.1 and 34.9+/-3.1, respectively) and nine healthy women (mean age: 40.3+/-11.6) underwent fMRI before and after antidepressant treatment. The fMRI paradigm contrasted a 1 minute rest period viewing non-erotic film with 4 minutes of sexual stimulation viewing an erotic video film. Data were analyzed by SPM 2. The relative number of pixels activated in each period was used as an index of activation. All depressed women were treated with mirtazapine (mean dosage: 37.5 mg/day) for 8 to 10 weeks. RESULTS: Levels of brain activity during sexual arousal in depressed women significantly increased with antidepressant treatment (p<0.05) in the regions of the hypothalamus (3.0% to 11.2%), septal area (8.6% to 27.8%) and parahippocampal gyrus (5.8% to 14.6%). Self-reported sexual arousal during visual sexual stimulation also significantly increased post-treatment, and severity of depressive symptoms improved, as measured by the BDI and HAMD-17 (p<0.05). CONCLUSION: These results show that sexual arousal dysfunction of depressed women may improve after treatment of depression, and that this improvement is associated with increased activation of the hypothalamus, septal area, and parahippocampal gyrus during sexual arousal.
Subject(s)
Female , Humans , Arousal , Brain , Depression , Hypothalamus , Magnetic Resonance Imaging , Mianserin , Neuroimaging , Parahippocampal Gyrus , Septum of BrainABSTRACT
OBJECTIVES: The purpose of this study was to investigate the stress coping strategies and psychological characteristics, such as combined psychopathology and tendency of symptom interpretation, in patients with somatization disorder. METHODS: Thirty patients meeting the criteria of DSM-IV somatization disorder were compared with thirty controls. We evaluated the subjects using Symptom Checklist-90-Revised (SCL-90-R), Somato-Sensory Amplification Scale (SSAS), Symptom Interpretation Questionnaire (SIQ), and The Ways of Stress Coping Questionnaire (SCQ). Independent t-test and Pearson correlation analysis were used. RESULTS: From the results of SCL-90-R subscales, the scores of somatization, obsession-compulsion, depression, anxiety, and psychoticism were significantly higher in patients with somatization disorder than normal controls. Somatization disorder patients had greater amplification of physical sensation in SSAS and significantly higher score in physical interpretation of SIQ compared with psychological or environmental interpretation. In the SCQ of somatization disorder patients, we observed generally lower levels of total coping scores than the control group and significant positive correlation between passive coping style of SCQ and psychological interpretation of SIQ. CONCLUSION: These results show that patients with somatization disorder have various psychopathology, greater amplification of physical sensation, physical interpretation tendency of symptoms, and insufficient copying strategy.
Subject(s)
Humans , Anxiety , Depression , Diagnostic and Statistical Manual of Mental Disorders , Psychopathology , Surveys and Questionnaires , Sensation , Somatoform DisordersABSTRACT
We present here a case in which functional MR imaging (fMRI) was done for a patient who developed retrograde psychogenic amnesia for a four year period of her life history after a severe stressful event. We performed the fMRI study for a face recognition task using stimulation with three kinds of face photographs: recognizable familiar faces, unrecognizable friends' faces due to the psychogenic amnesia, and unfamiliar control faces. Different activation patterns between the recognizable faces and unrecognizable faces were found in the limbic area, and especially in the amygdala and hippocampus.
Subject(s)
Humans , Female , Adult , Stress Disorders, Post-Traumatic , Magnetic Resonance Imaging , Hippocampus/physiology , Amygdala/physiology , Amnesia, Retrograde/diagnosisABSTRACT
PURPOSE: To identify the brain centers associated with visually evoked sexual arousal in the human brain, and to investigate the neural mechanism for sexual arousal using functional MRI (fMRI). MATERIALS AND METHODS: A total of 20 sexually potent volunteers consisting of 10 males (mean age: 24) and 10 females (mean age: 23) underwent fMRI on a 1.5 T MR scanner (GE Signa Horizon). The fMRI data were obtained from 7 slices (10 mm slice thickness) parallel to the AC-PC (anterior commissure and posterior commissure) line, giving a total of 511 MR images. The sexual stimulation consisted of a 1-minute rest with black screen, followed by a 4-minute stimulation by an erotic video film, and concluded with a 2-minute rest. The brain activation maps and their quantification were analyzed by the statistical parametric mapping (SPM 99) program. RESULTS: The brain activation regions associated with visual sexual arousal in the limbic system are the posterior cingulate gyrus, parahippocampal gyrus, hypothalamus, medial cingulate gyrus, thalamus, amygdala, anterior cingulate gyrus, insula, hippocampus, caudate nucleus, globus pallidus and putamen. Especially, the parahippocampal gyrus, cingulate gyrus, thalamus and hypothalamus were highly activated in comparison with other areas. The overall activities of the limbic lobe, diencephalon, and basal ganglia were 11.8%, 10.5%, and 3.4%, respectively. In the correlation test between brain activity and sexual arousal, the hypothalamus and thalamus showed positive correlation, but the other brain areas showed no correlation. CONCLUSION: The fMRI is useful to quantitatively evaluate the cerebral activation associated with visually evoked, sexual arousal in the human brain. This result may be helpful by providing clinically valuable information on sexual disorder in humans as well as by increasing the understanding of the neuroanatomical correlates of sexual arousal.
Subject(s)
Female , Humans , Male , Amygdala , Arousal , Basal Ganglia , Brain , Caudate Nucleus , Diencephalon , Globus Pallidus , Gyrus Cinguli , Hippocampus , Hypothalamus , Hypothalamus, Middle , Limbic System , Magnetic Resonance Imaging , Parahippocampal Gyrus , Putamen , Thalamus , VolunteersABSTRACT
PURPOSE: The present study utilized 3.0 Tesla functional MR imaging to identify and quantify the activated brain regions associated with visually evoked sexual arousal, and also to discriminate the gender differences between the cortical activation patterns in response to sexual stimuli. MATERIALS AND METHODS: A total of 24 healthy, right-handed volunteers, 14 males (mean age: 24) and 10 females (mean age: 23), with normal heterosexual function underwent functional MRI on a 3.0T MR scanner (Forte, Isole technique, Korea). The sexual stimulation consisted of a 1-minute rest with black screen, followed by a 3-minute stimulation by an erotic video film, and concluded with a 1-minute rest. The fMRI data was obtained from 20 slices (5 mm slice thickness, no gap) parallel to the AC-PC (anterior commissure and posterior commissure) line on the sagittal plane, giving a total of 2,100 images. The brain activation maps and the resulting quantification were analyzed by the statistical parametric mapping program, SPM 99. The mean-activated images were obtained from each individual activation map using one sampled t-test. The FALBA program, which is a new algorithm based on the pixel differentiation method, was used to identify and quantify the brain activation and lateralization indices with respect to the functional and anatomical terms. RESULTS: In both male and female volunteers, significant brain activation showed in the limbic areas of the parahippocampal gyrus, septal area, cingulate gyrus and thalamus. It is interesting to note that the septal areas gave a relatively lower activation ratio with high brain activities. On the contrary, the putamen, insula cortex, and corpus callosum gave a higher activation ratio with low brain activities. In particular, brain activation in the septal area, which was not reported in the previous fMRI studies under 1.5 Tesla, represents a distinct finding of this study using 3.0 T MR scanner. The overall lateralization index of activation shows left predominance (LI=35.3%) in the limbic system during sexual stimulation. The gender differences of brain activation in response to sexual arousal were characterized as follows. The activation area observed in males was the hypothalamus in the limbic system, whereas in females it was the cingulate gyrus, head of caudate nucleus, insula and corpus callosum. These findings reveal dissimilarities between males and females in neuronal responses to sexual arousal. As for the overall lateralization of activation in the limbic system, male volunteers gave a lateralization index that was greater than that of females by 300%. CONCLUSION: Our findings confirmed that neuroanatomical regions are associated with visually evoked sexual arousal and also with gender differences in response to sexual stimulation. Given that data from time-course traces of activation pattern and findings are observed by different stimuli, such as tactile and olfactory sense, it might be helpful to evaluate the neurophysiological mechanism for sexual arousal, and furthermore, to develop new diagnostic tools for sexual dysfunction and disorder.
Subject(s)
Female , Humans , Male , Arousal , Brain , Caudate Nucleus , Corpus Callosum , Gyrus Cinguli , Head , Heterosexuality , Hypothalamus , Limbic System , Magnetic Resonance Imaging , Neurons , Parahippocampal Gyrus , Putamen , Septum of Brain , Thalamus , VolunteersABSTRACT
PURPOSE: To identify, using functional MR imaging, distinct cerebral centers and to evaluate the neural mechanism associated with implicit and explicit retrieval of words during conceptual processing. MATERIALS AND METHODS: Seven healthy volunteers aged 21-25 (mean, 22) years underwent BOLD-based fMR imaging using a 1.5T Signa Horizon Echospeed MR system. To activate the cerebral cortices, a series of tasks was performed as follows: the encoding of two-syllable words, and implicit and explicit retrieval of previously learned words during conceptual processing. The activation paradigm consisted of a cycle of alternating periods of 30 seconds of stimulation and 30 seconds of rest. Stimulation was accomplished by encoding eight twosyllable words and the retrieval of previously presented words, while the control condition was a white screen with a small fixed cross. During the tasks we acquired ten slices (6 mm slice thickness, 1 mm gap) parallel to the AC-PC line, and the resulting functional activation maps were reconstructed using a statistical parametric mapping program (SPM 99). RESULTS: A comparison of activation ratios (percentages), based on the number of volunteers, showed that activation of Rhs-35, PoCiG-23 and ICiG-26, 30 was associated with explicit retrieval only; other brain areas were activated during the performance of both implicit and explicit retrieval tasks. Activation ratios were higher for explicit tasks than for implicit; in the cingulate gyrus and temporal lobe they were 30% and 10% greater, respectively. During explicit retrieval, a distinct brain activation index (percentage) was seen in the temporal, parietal, and occipital lobe and cingulate gyrus, and PrCeG-4, Pr/PoCeG-43 in the frontal lobe. During implicit retrieval, on the other hand, activity was greater in the frontal lobe, including the areas of SCA-25, SFG/MFG-10, IFG-44, 45, OrbG-11, 47, SFG-6, 8, and MFG-9, 46. Overall, activation was lateralized mainly in the left hemisphere during both implicit and explicit retrieval tasks. For explicit retrieval, the lateralization index was more than twice as high as for implicit retrieval. CONCLUSION: Our findings indicate that there is neuro-anatomical dissociation between implicit and explicit retrieval of words during conceptual processing, suggesting, on the basis of cognitive neuroscience, that the performance of implicit and explicit memory-related tasks involves different mechanisms.