Transvaginal sonography and surgical findings in the diagnosis of endometriosis individuals: A cross-sectional study

Background: Endometriosis is a challenging gynecological disease and a debilitating condition that profoundly affects the individual’s quality of life. Besides pathological confirmation, diagnostic laparoscopy has been internationally accepted as the standard method to identify the accurate mapping of endometriosis. Transvaginal sonography (TVS) is the first non-invasive imaging modality to estimate the severity of endometriosis. Objective: This study aimed to evaluate the accuracy of TVS in affected women compared with surgical findings. Materials and Methods: This retrospective cross-sectional study surveyed 170 women with deep infiltrating endometriosis (DIE) referred to the endometriosis part of the Avicenna Infertility Center, Tehran, Iran and they underwent TVS followed by laparoscopy. Recorded data of individuals under study in the medical database system were reviewed. Finally, the agreement rate was calculated for ultrasound reports and intraoperative (IO) findings regarding ovarian endometrium, ovarian adhesion, involvement of cul-de-sac, rectovaginal septum, and bowel and ureter. Results: 170 women with DIE entered the study. The agreement of TVS and IO findings were 86.76% for left ovarian endometriosis and 70.86% for right ovarian endometriosis, 93.90% for left ovarian adhesion, and 88.90% for right ovarian adhesion, 88.90% for a cul-de-sac, and 84.82% for bowel nodules. The findings, based on a laparoscopic assessment of the pelvic floor, were completely compatible with ultrasound reports (100%). Conclusion: TVS allows a preoperative evaluation in planning the surgical policy associated. TVS is beneficial for dedicated mapping of DIE; thus, an expert radiologist can aid the surgeon in preoperative evaluation and IO management. Key words: Endometriosis, Laparoscopy, Pathology

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans.

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework's diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona's assistance