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In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients. First, we extracted approximately 2,500 radiomic features from MR images with two regions of interest: the placenta and the uterus. In addition to analyzing two regions of interest, we dilated the placenta and uterus masks by 5, 10, 15, and 20 mm to gain insights from the myometrium, where the uterus and placenta overlap in the case of PAS. This study cohort includes 241 pregnant women. Of these women, 89 underwent hysterectomy while 152 did not; 141 with suspected PAS, and 100 without suspected PAS. We obtained an accuracy of 0.88 for predicting hysterectomy and an accuracy of 0.92 for classifying suspected PAS. The radiomic analysis tool is further validated, it can be useful for aiding clinicians in decision making on the care of pregnant women.

Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation. The segmentation performance of the algorithm was evaluated using MR images of 60 additional patients that were not involved in training. The average Dice similarity coefficients achieved for the uterine cavity and placenta were 92% and 80%, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of less than 1.1% compared to manual estimations. Automated segmentation, when incorporated into clinical use, has the potential to quantify, standardize, and improve placental assessment, resulting in improved outcomes for mothers and fetuses.

In severe cases, placenta accreta spectrum (PAS) requires emergency hysterectomy, endangering the life of both mother and fetus. Early prediction may reduce complications and aid in management decisions in these high-risk pregnancies. In this work, we developed a novel convolutional network architecture to combine MRI volumes, radiomic features, and custom feature maps to predict PAS severe enough to result in hysterectomy after fetal delivery in pregnant women. We trained, optimized, and evaluated the networks using data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively. We found the network using all three paths produced the best performance, with an AUC of 87.8, accuracy 83.3%, sensitivity of 85.0, and specificity of 82.5. This deep learning algorithm, deployed in clinical settings, may identify women at risk before birth, resulting in improved patient outcomes.

Introduction Current medical education promotes enhanced integration of various disciplines and early exposure to clinically relevant topics. Against this background, we have developed a team-based learning (TBL) module for medical students in the preclerkship period that integrates embryology, anatomy, and radiology of the head and neck region. Methods The module, which includes prereading assignments, readiness assurance tests, and an application exercise, focuses on the development of the head and neck region. Students were asked to consolidate their knowledge of the topic—acquired as part of their regular curriculum—and to apply this knowledge to cases of cervical cysts and cleft lip/palate. Results The TBL module was developed for a class of 234 students. The students performed well in the TBL module. Although many students perceived the session as time-consuming, the majority of students evaluated it as relevant for their understanding of the course material and a valuable adjunct to their course. Discussion To our knowledge, as of this writing, no TBL modules have been published that focus on the integration of complex embryological topics with anatomy and radiology and that are suitable for medical students at the beginning of their education. Therefore, the presented TBL module fills a gap in material available to educators in the field.

Background: Cysticercosis, due to the parasite Taenia solium, can involve any organ. When central nervous system infection occurs, signs and symptoms depend on the location of the cerebral lesions. Most patients develop seizures, focal symptoms, or headaches with nausea and vomiting. Case: A case of extraparenchymal (intraventricular) cysticercosis was diagnosed in a patient at term who presented with acute alteration in mental status. Ventriculostomy was performed because of acute obstructive hydrocephalus. Labor ensued and was augmented with oxytocin. Intrapartum management included magnesium sulfate seizure prophylaxis and corticosteroids. Intracranial pressures ranged between 4 and 12 cm H2O peripartum with approximately 300 mL of cerebrospinal fluid drained over the first 24 hours. Postpartum management included craniotomy with resection of a larval cyst and oral praziquantel therapy. Conclusion: This case describes an uncommon presentation of neurocysticercosis that should be considered in gravidas with acute mental status changes.