Deep Learning-Based Computed Tomography Image Features in the Detection and Diagnosis of Perianal Abscess Tissue

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.

Complicated anorectal sepsis: Validation of scoring system for predicting anorectal sepsis severity

Anorectal sepsis is a common and potentially serious medical condition characterized by infection and inflammation of the anal canal and surrounding tissues. However, the lack of standardized and comprehensive scoring systems specifically tailored for predicting the severity of anorectal sepsis poses challenges in clinical practice. This study aimed to develop and validate a scoring system for predicting the severity of anorectal sepsis by incorporating relevant patient factors. A retrospective cohort study was conducted at Mansoura University Hospital, a tertiary care center, over a period of 5 years. The study population consisted of 330 patients diagnosed with anorectal sepsis during the study period. A scoring system was developed using multiple variables, with each variable assigned a specific score based on its clinical significance and weight in predicting disease severity. The developed scoring system's predictive performance was evaluated using receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve to assess discriminative ability. Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Chi-square tests or t tests were performed to assess differences between non-severe and severe anal sepsis groups. The scoring system consisted of 12 variables, with a maximum total score of 18. The logistic regression analysis revealed significant associations between localized swelling, presentation within 72 hours, multiple drainage sessions, and severe anorectal sepsis. The ROC analysis showed an area under the curve of 0.85, indicating good discriminative ability of the scoring system. The scoring system was developed and validated in a single center, which may limit its generalizability to other settings. The scoring system demonstrated good predictive performance and can be a valuable tool for clinicians in assessing disease severity, guiding treatment decisions, and identifying high-risk patients.

Intervention Effect of Evidence-Based Nursing on Postoperative Recovery of Vacuum Sealing Drainage in Patients with High Perianal Abscess with Magnetic Resonance Imaging Sequence Images

The purpose of this study was to evaluate the intervention effect of evidence-based nursing (EBN) on vacuum sealing drainage (VSD) recovery of patients with high perianal abscess after vacuum sealing drainage based on magnetic resonance imaging (MRI) sequence images. 60 patients with high perianal abscess were selected, and 30 patients before VSD were selected as the control group. Routine nursing was implemented in the control group, 30 patients after VSD were observed, and EBN was implemented in the observation group. The detection rates of various types of perianal abscess with different sequence combinations were studied, and the effects of EBN on pain and anal function scores of perianal abscess patients were analyzed. Anal function and defecation were assessed, and postoperative complications were calculated. Different combinations of MRI sequences can reach higher detection rates of intersphincter abscess and ischial anal abscess. The observation group had better pain relief and anal function recovery. The complication rate of the observation group was 16.67%, which was significantly lower than that of the control group (P < 0.05). It was confirmed that different MRI sequence combinations had higher detection rates for intersphincter abscess and ischial fossa abscess. EBN can promote the recovery of anal function and reduce complications in patients with perianal abscess.

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group (P < 0.05); the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group (P < 0.05). Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.