Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

CONTEXT.­: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.­: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.­: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.­: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.­: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.

Comparison of marginal bone loss and patient satisfaction in single and double-implant assisted mandibular overdenture by immediate loading.

PURPOSE: The purpose of this study was to compare the coronal bone level and patient satisfaction in 1-implant and 2-implant assisted mandibular overdentures. MATERIALS AND METHODS: Twenty patients who had maladaptive mandibular dentures were treated in this study. Patients were randomly divided into two groups. The first group received 1 implant (Simple line II, Implantium, South Korea) in their mandibular midline and the second group received 2 implants in their B and D regions (according to Misch's category). If the primary stability of each implant was at least 60 ISQ, ball attachment was placed and denture relined with soft liner. After 6 weeks, retentive cap incorporated with hard acrylic resin. In the 6 and 12 months recalls, periapical digital radiograph were made and visual analogue scale questionnaires were used to record patient satisfaction. The Friedman test was done for comparing the presurgical and postsurgical parameters in each group and the U-Mann Whitney test (P<.05) was done for comparison of post-treatment results between the two groups. RESULTS: All implants achieved sufficient primary stability to be immediately loaded. Patient satisfaction was high, and there were no significant differences between two groups (P>.05). In addition, mean marginal bone loss was 0.6 ± 0.67 mm in the first group and 0.6 ± 0.51 mm in the second group, after 12 month. Mean marginal bone loss showed no significant differences between two groups. CONCLUSION: This preliminary one-year result indicated that mandibular overdentures anchored to a single implant can be a safe and cost-effective method as a starting step for implant-overdenture treatment.