Repetitive Transcranial Magnetic Stimulation for Chronic Prostatitis/Chronic Pelvic Pain Syndrome: A Prospective Pilot Study

Comparative efficacy of radical prostatectomy and radiotherapy in the treatment of high-risk prostate cancer

BACKGROUND

Both radical prostatectomy and radiation therapy are effective in controlling the condition of patients with hormone-resistant prostate cancer (HRPCa). However, there is limited research on the prognosis and quality of life of HRPCa patients after different treatment modalities.

OBJECTIVE

To explore the efficacy of radical prostatectomy (RP) and radiotherapy (RT), when treating high-risk prostate cancer (HRPCa).

METHODS

Overall 103 HRPCa patients were included and were divided into RP group and RT group according to different treatment methods. The propensity score matching method (PSM) was used to balance the baseline data of the two groups and match 34 patients in each group. The prognosis, quality of life, and basic efficacy of patients were compared.

RESULTS

After intervention, the disease-free survival rate of the RT group was higher than that of the RP group (79.41% vs. 55.88%, p= 0.038). Quality of life scores between the two treatment methods had no difference before intervention (p> 0.05), but higher in RT group than that of the RP group after intervention (p< 0.05). After treatment, there was no statistically significant difference in total effective rate of treatment between two groups (44.12% vs. 58.82%, p> 0.05), but the disease control rate was significantly higher in RT group (94.12% vs. 76.47%, p= 0.040).

CONCLUSION

Radical radiotherapy is effective in the clinical treatment of HRPCa patients, with a higher disease-free survival rate and improved quality of life after treatment, and is worth promoting.

Impact of Titanium Skull Plate on Transcranial Magnetic Stimulation: Analysis of Induced Electric Fields

BACKGROUND

Implanted titanium skull plates (TSPs) in cranioplasty are used to replace or reconstruct areas of the skull that have been damaged or removed due to trauma, surgery, or other medical conditions. However, the presence of a TSP in the head may influence the distribution of the electric field induced during transcranial magnetic stimulation (TMS) procedures. The purpose of this study was to determine how the presence of TSP would interfere with TMS-induced cortical electric fields.

METHODS

The TMS with a figure-of-eight coil was applied to a realistic head model with TSPs. The distribution of the induced electric field in head tissues was calculated by employing the impedance method, and the results were compared with that of a normal head without TSP.

RESULTS

Simulation results show that the distribution of the induced electric field has changed greatly for the head model with TSP. The maximum value of the induced electric field in head tissues was present under one of the circular coil wings rather than in the tissues beneath the junction of the two wings of the Fo8 coil.

CONCLUSIONS

The induced electric field in deep brain regions was increased for the head model with TSP, which could potentially lead to deep brain stimulation. Since the presence of metallic TSP can greatly influence the distribution of the induced electric field in TMS applications, it is important to adjust the treatment scheme when considering TMS for individuals with cranial titanium plates.

Serial changes in diffusion tensor imaging metrics and therapeutic effects of repetitive transcranial magnetic stimulation in post-traumatic headache and depression: A case report

BACKGROUND

Mild traumatic brain injury patients commonly complain headache and central pain, and the pain accompanies depressive mood change. This case study reports the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS) in mild traumatic brain injury patient with headache and depression through objective serial changes of diffusion tensor imaging (DTI).

METHODS

The 51-year-old man complained of headache and depression despite conventional treatment for 13 months. We applied 15 times rTMS on the left dorsolateral prefrontal cortex. We checked the pain and depression through numeric rating scale (NRS) and Beck depression inventory (BDI) when admission, discharged, and 1 month after discharge. DTI was performed 3 times; before, during-day of rTMS 6th stimulation, and after-day of rTMS 15th stimulation. Then the reconstructed White matter related to pain and depression was obtained.

RESULTS

NRS and BDI showed significant improvement and it was maintained 1 year after discharge. DTI-based metrics of the White matters related to pain and depression gradually increased before - during - after rTMS.

CONCLUSION

Studies focused on examining changes in pain, depression and DTI-based metrics of White matter are rare. This case is significant in that not only pain and depression improved after the rTMS, but also serial changes in White matter were observed in DTI.

Artificial Intelligence-Based Patient Monitoring System for Medical Support

PURPOSE

In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user's urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder.

METHODS

Our approach included the creation of AI-based recognition technology that automatically logs users' urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology.

RESULTS

The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%.

CONCLUSION

In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.

Noninvasive brain stimulation in the treatment of functional urological and pelvic floor disorders: A scoping review

INTRODUCTION

Functional pelvic floor disorders (PFD) such as bowel and bladder dysfunctions can be challenging to manage with our current therapeutic modalities. Recently, noninvasive brain stimulation has emerged as a novel strategy for noninvasive pelvic floor management. Here, we assessed the current state of research on this topic.

METHODS

A scoping review was conducted with Pubmed, Web of Science, and Embase, in conjunction with clinicaltrials.gov, encompassing all manuscripts published without past time limit up until June 30, 2022.

RESULTS

Of the 880 abstracts identified in a blind selection by two reviewers, 14 publications with an evidence level of 1 or 2 (Oxford scale) were eligible and included in this review. Review articles, case reports (<5 patients), letters, and protocol studies were excluded. PFDs were described as either pelvic pain or lower urinary tracts symptoms (LUTS) with repeated transcranial magnetic stimulation (rTMS) as the most common treatment modality. Despite heterogeneous therapeutic protocols, significant improvements were observed such as reduction in postvoid residual of urine, increased bladder capacity, improved voiding flow paraments, and decreased chronic pelvic, and bladder pain. No appreciable adverse effects were noted. However, low sample populations allowed only provisional conclusions.

CONCLUSION

Noninvasive transcranial neurostimulation for LUTS and pelvic pain is emerging as an effective tool for clinicians to utilize in the future. Further investigation is needed to elucidate the full significance of the indicated outcomes.

Applications of artificial intelligence in urological setting: a hopeful path to improved care

Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.

Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

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.

Development of urination recognition technology based on Support Vector Machine using a smart band

The purpose of this study was to explore the feasibility of a urination management system by developing a smart band-based algorithm that recognizes the urination interval of women. We designed a device that recognizes the time and interval of urination based on the patient’s specific posture and posture changes. The technology used for recognition applied the Radial Basis Function kernel-based Support Vector Machine, a teaching and learning method that facilitates multidimensional analysis by simultaneously judging the characteristics of complex learning data. In order to evaluate the performance of the proposed recognition technique, we compared actual urination and device-sensed urination. An experiment was performed to evaluate the performance of the recognition technology proposed in this study. The efficacy of smart band monitoring urination was evaluated in 10 female patients without urination problems. The entire experiment was performed over a total of 3 days. The average age of the participants was 28.73 years (26–34 years), and there were no signs of dysuria. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 91.0%, proving the robustness of the proposed algorithm. This urination behavior recognition technique shows high accuracy and can be applied in clinical settings to characterize urination patterns in female patients. As wearable devices develop and become more common, algorithms that detect specific sequential body movement patterns that reflect specific physiological behaviors could become a new methodology to study human physiological behavior.

The role of the multi-disciplinary team and multi-disciplinary therapeutic protocol in the management of the chronic pelvic pain: There is strenght in numbers!

INTRODUCTION

The aim of the study is to evaluate the effectiveness of a Multi-disciplinary team (MDT) and multi-disciplinary approach in the treatment of Chronic Pelvic Pain (CPP).

METHODS

The data of all consecutive patients referred for a CPP from 11/2016 to 2/2019 has been prospectively collected. The sample was divided in two groups: Group A, made by patients managed after the institution of our MDT, and Group B, made of patients managed before this date. The MDT is composed by three urogynecologists, a psychologist and a physiotherapist. All Group A patients underwent a weekly bladder instillation with dimethyl sulfoxide (DMSO), kinesiotherapy for trigger points and Percutaneous Tibial Nerve Stimulation for 10 consecutive weeks. Patients were asked to perform a self-treatment following the Stanford Protocol and to adhere to a specific diet. All Group B patients were managed only with DMSO instillations and a strict diet.

RESULTS

The Group A was made of 41 females and 6 males while the Group B was made of 38 females and 5 males. The Group A patients showed a statistically significant improvement in the Pelvic Pain Urgency Frequency, in the frequency times reported at the 6 months voiding diary, and a better Patient Global Impression of Improvement.

CONCLUSIONS

Our data support the efficacy of the MDT in the management of CPP. The multimodal approach might represent an effective and reproducible non-invasive option to manage successfully CPP.