Deciphering ovarian torsion: insights from CT imaging analysis.

PURPOSE: In the milieu of emergency medicine, pelvic and lower abdominal pain present recurrently, with ovarian torsion posing a formidable diagnostic quandary amid multifarious etiologies. Given the burgeoning reliance on CT in acute care settings, it invariably assumes primacy as the principal imaging modality. This study endeavors to elucidate the CT imaging manifestations encountered by surgically confirmed ovarian torsion patients and utilizing CT to differentiate necrosis. METHODS: A retrospective analysis (January, 2015- April, 2019) utilizing hospital archives was conducted on patients diagnosed with ovarian torsion, post-surgery. Inclusion criteria encompassed patients who underwent CT examinations within one week of diagnosis. A large array of CT findings encompassing midline orientation, uterine deviation, intraovarian hematoma/mass, and multiple others were systematically documented. RESULTS: 90 patients were diagnosed with ovarian torsion- 53 (59%) had CT within one week of diagnosis, 41(77%) underwent a CT with IV contrast and 12 (23%) without IV contrast. Mean age was 43 years (range 19-77 years), with near equal distribution of involvement of each ovary. Mean maximum ovarian diameter was 11.7 ± 6.3 cm (4.2-34.8 cm). Most common imaging features include the presence of thickened pedicle (43/53, 81%), midline ovary (41/53, 77%), presence of thickened fallopian tube (31/49, 63%), and ipsilateral uterine deviation (33/53, 62%). Based on contemporaneous imaging report, torsion was diagnosed in 25/ 53 studies giving a sensitivity of 47%. CONCLUSION: Enlarged ovarian dimensions (> 3.0 cm), thickened vascular pedicle or fallopian tube, midline ovarian disposition with ipsilateral uterine deviation, and the presence of a whirlpool sign emerged as predominant CT imaging features in surgically confirmed ovarian torsion cases, serving as pivotal diagnostic aides for radiologists. Concomitant pelvic free fluid and intraovarian hematoma signify necrotic changes, indicative of ischemic severity and disease progression.

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and mortality. However, PE remains subject to misdiagnosis. METHODS: We retrospectively identified 251 CTPAs performed at a tertiary care hospital between January 2018 to January 2021. The scans were classified as positive (n = 55) and negative (n = 196) for PE based on the annotations made by board-certified radiologists. A fully anonymized CT slice served as input for the detection of PE by the 2D segmentation model comprising U-Net architecture with Xception encoder. The diagnostic performance of the model was calculated at both the scan and the slice levels. RESULTS: The model correctly identified 44 out of 55 scans as positive for PE and 146 out of 196 scans as negative for PE with a sensitivity of 0.80 [95% CI 0.68, 0.89], a specificity of 0.74 [95% CI 0.68, 0.80], and an accuracy of 0.76 [95% CI 0.70, 0.81]. On slice level, 4817 out of 5183 slices were marked as positive for the presence of emboli with a specificity of 0.89 [95% CI 0.88, 0.89], a sensitivity of 0.93 [95% CI 0.92, 0.94], and an accuracy of 0.89 [95% CI 0.887, 0.890]. The model also achieved an AUROC of 0.85 [0.78, 0.90] and 0.94 [0.936, 0.941] at scan level and slice level, respectively for the detection of PE. CONCLUSION: The development of an AI model and its use for the identification of pulmonary embolism will support healthcare workers by reducing the rate of missed findings and minimizing the time required to screen the scans.

Utility of 3D-T2 space MRI sequence in diagnosing a rare cause of lower backache: horseshoe cord and meningocoele manqué in a case of composite split cord malformation.

Diastematomyelia is the asymmetric or symmetric lateral duplication of the spinal cord into two hemicords. Pang divided it into three types: type-1, type-2 and composite split cord malformation (SCM). Composite SCMs are uncommon and are defined by the coexistence of multiple types of split cord with normal cord in between. When partially bridged, they are called a horseshoe cord. We report a case of a young woman who presented with backache and was diagnosed with composite SCM with horseshoe cord and type-2 SCM with intervening normal cord. In our case, 3D-SPACE due to its superior topographical evaluation, allowed us to visualise and characterise the thin meningocoele manque bands and detect horseshoe cords, asymmetric cords and demarcate the precise extent of syrinx, which were suboptimally imaged on isolated T2 and T1WI sequences. If left untreated during surgery, bands can be the potential cause for persistent backache.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence.

Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.