Deep Learning-Based Automated Imaging Classification of ADPKD.

Introduction: The Mayo imaging classification model (MICM) requires a prestep qualitative assessment to determine whether a patient is in class 1 (typical) or class 2 (atypical), where patients assigned to class 2 are excluded from the MICM application. Methods: We developed a deep learning-based method to automatically classify class 1 and 2 from magnetic resonance (MR) images and provide classification confidence utilizing abdominal T 2 -weighted MR images from 486 subjects, where transfer learning was applied. In addition, the explainable artificial intelligence (XAI) method was illustrated to enhance the explainability of the automated classification results. For performance evaluations, confusion matrices were generated, and receiver operating characteristic curves were drawn to measure the area under the curve. Results: The proposed method showed excellent performance for the classification of class 1 (97.7%) and 2 (100%), where the combined test accuracy was 98.01%. The precision and recall for predicting class 1 were 1.00 and 0.98, respectively, with F 1 -score of 0.99; whereas those for predicting class 2 were 0.87 and 1.00, respectively, with F 1 -score of 0.93. The weighted averages of precision and recall were 0.98 and 0.98, respectively, showing the classification confidence scores whereas the XAI method well-highlighted contributing regions for the classification. Conclusion: The proposed automated method can classify class 1 and 2 cases as accurately as the level of a human expert. This method may be a useful tool to facilitate clinical trials investigating different types of kidney morphology and for clinical management of patients with autosomal dominant polycystic kidney disease (ADPKD).

Management of a Nulliparous IVF Patient with a Declined Ovarian Reserve After Discovery of an Atypical Ovarian Endometriotic Cyst.

Purpose: Endometriosis (EM) is a common cause of infertility, and an ovarian endometriotic cyst may affect the ovarian reserve, ovulation, and endometrial receptivity. The majority of EM cases are benign; however, EM may also be prone to malignant transformation, associated with infiltrative growth, and recurrent or distant metastasis. In this study, we report the management of an atypical cyst discovered through ovarian endometriotic cyst puncture prior to controlled ovarian stimulation (COS). Case Presentation: The patient required in vitro fertilization treatment due to EM and bilateral fallopian tube obstruction. Prior to initiating gonadotropin (Gn) treatment, the right ovarian EM cyst was punctured; cytological pathology of the obtained fluid revealed an atypical morphology. Subsequently, the case was discussed with the patient and ethically reviewed. Gn was initiated according to the patient's wishes, and six day-3 embryos were finally obtained and cryopreserved. Afterwards, laparoscopic cystectomy of the ovarian endometrioma revealed no malignant transformation. The patient achieved clinical pregnancy after resuscitation and transplantation of the embryo. Conclusion: In summary, patients with EM-associated infertility are at risk of ovarian cancer formation when undergoing assisted reproduction treatment; therefore, this risk should be evaluated and minimized before initiating such treatment.