Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm

Effect of Low-Frequency Pulsed Electrotherapy Combined with Acupoint Nursing on Postpartum Urinary Retention in Patients with Vaginal Delivery

INTRODUCTION AND HYPOTHESIS

This study was carried out to investigate the effect of low-frequency pulsed electrotherapy combined with acupoint massage on postpartum urinary retention (PUR).

METHODS

The patients were divided into control group, intervention group 1, and intervention group 2 according to the nursing method. The control group received conventional postpartum care, intervention group 1 received conventional postpartum care and low frequency pulsed electrotherapy, and intervention group 2 received conventional postpartum care, low-frequency pulsed electrotherapy, and Shuidao point massage. The bladder function, comfort score, and quality of life score before and after intervention were compared among the three groups.

RESULTS

The bladder function, comfort level, and quality of life of intervention group 1 and intervention group 2 after nursing were significantly better than those of the control group. In addition, intervention group 2 had better bladder function than intervention group 1, with lower residual urine volume and higher bladder compliance. In the Kolcaba score, the mental dimension of intervention group 2 was significantly higher than that of intervention group 1. In terms of QOL scores, the social function, physical function, and state of material life scores of intervention group 2 were significantly higher than those of intervention group 1.

CONCLUSIONS

Low-frequency pulsed electrotherapy combined with acupoint massage can significantly improve the bladder function, comfort, and quality of life of patients with PUR.

Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders

Background

Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and lowering quality of life. Treating physicians encounter numerous challenges attributed to the complex nature of pelvic anatomy, limitations of diagnostic techniques, and lack of available resources. This article is an overview of the current state of artificial intelligence (AI) in tackling the difficulties of managing benign anorectal disorders and pelvic floor disorders.

Methods

A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed database to identify all potentially relevant studies published from January 2000 to August 2023. Search queries were built using the following terms: AI, machine learning, deep learning, benign anorectal disease, pelvic floor disorder, fecal incontinence, obstructive defecation, anal fistula, rectal prolapse, and anorectal manometry. Malignant anorectal articles and abstracts were excluded. Data from selected articles were analyzed.

Results

139 articles were found, 15 of which met our inclusion and exclusion criteria. The most common AI module was convolutional neural network. researchers were able to develop AI modules to optimize imaging studies for pelvis, fistula, and abscess anatomy, facilitated anorectal manometry interpretation, and improved high-definition anoscope use. None of the modules were validated in an external cohort.

Conclusion

There is potential for AI to enhance the management of pelvic floor and benign anorectal diseases. Ongoing research necessitates the use of multidisciplinary approaches and collaboration between physicians and AI programmers to tackle pressing challenges.

Neurobiological insights into lower urinary tract dysfunction: evaluating the role of brain-derived neurotrophic factor

Lower urinary tract dysfunction (LUTD) encompasses a range of debilitating conditions that affect both sexes and different age groups. Understanding the underlying neurobiological mechanisms contributing to LUTD has emerged as a critical avenue for the development of targeted therapeutic strategies. Brain-derived neurotrophic factor (BDNF), a prominent member of the neurotrophin family, has attracted attention due to its multiple roles in neural development, plasticity, and maintenance. This review examines the intricate interplay between neurobiological factors and LUTD, focusing on the central involvement of BDNF. The review emphasizes the bidirectional relationship between LUTD and BDNF and explores how LUTD-induced neural changes may affect BDNF dynamics and vice versa. Growth factor therapy and the combined administration of controlled release growth factors and stem cells are minimally invasive treatment strategies for neuromuscular injury. Among the many growth factors and cytokines, brain-derived neurotrophic factor (BDNF) plays a prominent role in neuromuscular repair. As an essential neurotrophin, BDNF is involved in the modulation of neuromuscular regeneration through tropomyosin receptor kinase B (TrkB). Increasing BDNF levels facilitates the regeneration of the external urethral sphincter and contributes to the regulation of bladder contraction. Treatments targeting the BDNF pathway and sustained release of BDNF may become novel treatment options for urinary incontinence and other forms of lower urinary tract dysfunction. This review discusses the applications of BDNF and the theoretical basis for its use in the treatment of lower urinary tract dysfunction, including urinary incontinence (UI), overactive bladder (OAB), and benign prostatic hyperplasia (BPH), and in the clinical diagnosis of bladder dysfunction.

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research.

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.