RESUMO
PURPOSE: Identification of mature sperm at microdissection testicular sperm extraction (mTESE) is a crucial step of sperm retrieval to help patients with non-obstructive azoospermia (NOA) proceed to intracytoplasmic sperm injection. Touch print smear (TPS) cytology allows immediate interpretation and prompt sperm identification intraoperatively. In this study, we leverage machine learning (ML) to facilitate TPS reading and conquer the learning curve for new operators. MATERIALS AND METHODS: One hundred seventy-six microscopic TPS images from the testicular specimen of patients with azoospermia at Taipei Veterans General Hospital were retrospectively collected, including categories of Sertoli cell, primary spermatocytes, round spermatids, elongated spermatids, immature sperm, and mature sperm. Among them, 118 images were assigned as the training set and 29 images as the validation set. RetinaNet (Lin et al. in IEEE Trans Pattern Anal Mach Intell. 42:318-327, 2020), a one-stage detection framework, was adopted for cell detection. The performance was evaluated at the cell level with average precision (AP) and recall, and the precision-recall (PR) curve was displayed among an independent testing set that contains 29 images that aim to assess the model. RESULTS: The training set consisted of 4772 annotated cells, including 1782 Sertoli cells, 314 primary spermatocytes, 443 round spermatids, 279 elongated spermatids, 504 immature sperm, and 1450 mature sperm. This study demonstrated the performance of each category and the overall AP and recall on the validation set, which were 80.47% and 96.69%. The overall AP and recall were 79.48% and 93.63% on the testing set, while increased to 85.29% and 93.80% once the post-meiotic cells were merged into one category. CONCLUSIONS: This study proposed an innovative approach that leveraged ML methods to facilitate the diagnosis of spermatogenesis at mTESE for patients with NOA. With the assistance of ML techniques, surgeons could determine the stages of spermatogenesis and provide timely histopathological diagnosis for infertile males.
RESUMO
Early and accurate prediction of endotracheal tube (ETT) location is pivotal for critically ill patients. Automatic and timely detection of faulty ETT locations from chest X-ray images may avert patients' morbidity and mortality. Therefore, we designed convolutional neural network (CNN)-based algorithms to evaluate ETT position appropriateness relative to four detected key points, including tracheal tube end, carina, and left/right clavicular heads on chest radiographs. We estimated distances from the tube end to tracheal carina and the midpoint of clavicular heads. A DenseNet121 encoder transformed images into embedding features, and a CNN-based decoder generated the probability distributions. Based on four sets of tube-to-carina distance-dependent parameters (i.e., (i) 30-70 mm, (ii) 30-60 mm, (iii) 20-60 mm, and (iv) 20-55 mm), corresponding models were generated, and their accuracy was evaluated through the predicted L1 distance to ground-truth coordinates. Based on tube-to-carina and tube-to-clavicle distances, the highest sensitivity, and specificity of 92.85% and 84.62% respectively, were revealed for 20-55 mm. This implies that tube-to-carina distance between 20 and 55 mm is optimal for an AI-based key point appropriateness detection system and is empirically comparable to physicians' consensus.