Computer-assisted interpretation of electromyograms of corpora cavernosa using fuzzy logic

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review

Despite the high prevalence of erectile dysfunction, patients are reluctant to seek medical advice, which leads to low diagnostic rates in clinical practice. Artificial intelligence has been widely applied in the diagnosis of many diseases and may alleviate the situation. However, the applications of artificial intelligence in erectile dysfunction have not been reviewed to date. Therefore, the assistance from artificial intelligence needs to be summarized. In this review, 418 publications before January 10, 2021, regarding artificial intelligence applications in diagnosing and predicting erectile dysfunction, were retrieved from five databases, including PubMed, EMBASE, the Cochrane Library, and two Chinese databases (WANFANG and CNKI). In addition, the reference lists of the included studies or relevant reviews were checked to avoid bias. Finally, 30 articles were reviewed to summarize the current status, merits, and limitations of applying artificial intelligence in diagnosing and predicting erectile dysfunction. The results showed that artificial intelligence contributed to developing novel diagnostic questionnaires, equipment, expert systems, classifiers by images and predictive models. However, most of the included studies were not subjected to external validations, resulting in doubt on the generalizability. In the future, more rigorously designed studies with high-quality datasets for erectile dysfunction are required.

Is dorsale penile vein ligation (dpvl) still a treatment option in veno-occlusive dysfunction?

PURPOSE

To determine the long-term results of the DPVL for the treatment of venous impotence.

PATIENTS AND METHODS

The long term results of DPVL in 134 patients were interviewed. The mean age was 39.2 (range 21-72). Power color doppler imaging, pharmacocavernography/dynamic cavernosometry were performed. Postoperatively, all the patients were controlled in the 6th month, first year and, if possible, once a year. The mean follow-up was 54.8 (14-76) months. Postoperative outcomes were classified into three groups: complete spontaneous erection (CR), response to pharmacotherapy (PR) or no satisfactory improvement (NR).

RESULTS

The short-term success in the 6th month according to above mentioned was 38.8%, 18.6% and 42.5%; and in the first year was 19.4%, 14.9% and 65.6% respectively. The result in 35 patients whose follow-up was at least 5 years (mean 67 months) was 11.4%, 14.3% and 74.3% respectively. Positive prognostic factors were preoperative age <40, duration of erectile dysfunction <2 years, non-smoker patients, non neurogenic disease and distal disease. With all these parameters present, long-term success (CR, PR) rose from 33.6% to 55.9% (P < 0.001).

CONCLUSIONS

Long-term success for unselected patients undergoing DPVL is disappointing; however, careful patient selection significantly improves long-term results.

The role of corpus cavernosum electromyography

Although corpus cavernosum electromyography presents great theoretical appeal as a possible direct clinical measure of penile neurogenic function, it has still not been adopted as a routine diagnostic test in the evaluation of patients with erectile dysfunction. This is because of the considerable technical and interpretative problems associated with the method. This review covers some of the more important research and clinical papers published since its first description in 1989, with a special focus on recent advances in this field.