Reply to Commentary on “Repetitive Transcranial Magnetic Stimulation for Chronic Prostatitis/Chronic Pelvic Pain Syndrome: A Prospective Pilot Study”

Nikkola, Jussi; Holm, Anu; Seppänen, Marjo; Joutsi, Teemu; Rauhala, Esa; Kaipia, Antti

doi:10.5213/inj.2040280.140

Cited by 13 publications

(15 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The AI diagnosis system [14][15][16][17][18][19][20][21] primarily performs the lesion classification step through the convolution neural network (CNN) algorithm based on the input data. In combination, upgrading is performed through the generative adversarial network (GAN) algorithm, and personalized lesions can be tracked in conjunction with the personal lesion tracking system.…”

Section: Ai (Cnn+gan) Diagnostic Systemmentioning

confidence: 99%

Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention

Kim

Heo²,

Eun

et al. 2022

Int Neurourol J

View full text Add to dashboard Cite

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.

show abstract

Section: Ai (Cnn+gan) Diagnostic Systemmentioning

confidence: 99%

Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention

Kim

Heo²,

Eun

et al. 2022

Int Neurourol J

View full text Add to dashboard Cite

show abstract

“…Various methods and learning algorithms for motion recognition have been proposed. Most studies have used static algorithms such as ANNs (Karmonik et al, 2019;Kim et al, 2020;Nikkola et al, 2020;Prabhakar et al, 2019) and K-means clustering (Baser et al, 2020;Fraley and Raftery, 2002;Moon and Cho, 2021), or dynamic time warping (Powar and Chemmangat, 2019) in combination with algorithms. However, a time series algorithm should be used to predict or classify dynamically changing time series data.…”

Section: Ai In Bladder Diarymentioning

confidence: 99%

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

Eun¹,

Kim²,

Kim³

2021

J Exerc Rehabil

View full text Add to dashboard Cite

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.

show abstract

“…symptoms (2). Tricyclic antidepressants, cognitive behavior therapy, neuromodulators, and pelvic floor kinesitherapy, stem cells and low-energy shock waves are only some of the proposed managements (3)(4)(5)(6)(7)(8)(9). However, the fact that no definite effective treatments for CPP have been identified to date further complicates the scenario and makes most of urologists are not confident with the management of this disease and its available therapies (10).…”

Section: Original Papermentioning

confidence: 99%

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!

Centemero

Rigatti

Giraudo

et al. 2021

Arch Ital Urol Androl

View full text Add to dashboard Cite

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.

show abstract

Reply to Commentary on “Repetitive Transcranial Magnetic Stimulation for Chronic Prostatitis/Chronic Pelvic Pain Syndrome: A Prospective Pilot Study”

Cited by 13 publications

References 4 publications

Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention

Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention

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

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!

Contact Info

Product

Resources

About