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Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists.

Today, ubiquitous digital communication systems do not have an intuitive, natural way of communicating emotion, which, in turn, affects the degree to which humans can emotionally connect and interact with one another. To address this problem, a more natural, intuitive, and implicit emotion communication system was designed and created that employs asymmetry-based EEG emotion classification for detecting the emotional state of the sender and haptic feedback (in the form of tactile gestures) for displaying emotions for a receiver. Emotions are modeled in terms of valence (positive/negative emotions) and arousal (intensity of the emotion). Performance analysis shows that the proposed EEG subject-dependent emotion classification model with Free Asymmetry features allows for more flexible feature-generation schemes than other existing algorithms and attains an average accuracy of 92.5% for valence and 96.5% for arousal, outperforming previous-generation schemes in high feature space. As for the haptic feedback, a tactile gesture authoring tool and a haptic jacket were developed to design tactile gestures that can intensify emotional reactions in terms of valence and arousal. Experimental study demonstrated that subject-independent emotion transmission through tactile gestures is effective for the arousal dimension of an emotion but is less effective for valence. Consistency in subject-dependent responses for both valence and arousal suggests that personalized tactile gestures would be more effective.

e14052 Background: The need for accurate pathological identification and quantitation of prostate cancer (PC) following neoadjuvant treatment with androgen deprivation therapy (ADT) and androgen receptor antagonists is increasing as PC treatment continues to evolve. In clinical practice, pathological assessment of residual tumor is a tedious and time-consuming process due to the volume of tissue from radical prostatectomy (RP). In addition, neoadjuvant treatments can greatly alter both benign and neoplastic prostate tissue morphology making the pathology assessment difficult for even specialized pathologists. Paige Prostate 1.0 is a clinical-grade artificial intelligence (AI) system for PC detection. It was trained and evaluated in over 50,000 prostate biopsy slides with validation across more than 800 institutions worldwide using multiple slide scanners. Methods: We evaluated the performance of Paige Prostate 1.0 at identifying prostatic tumor on 64 hematoxylin and eosin stained slides exhibiting neoadjuvant treatment effect from apalutamide, enzalutamide, and/or ADT. Results: Analysis of the receiver operating characteristic curve demonstrated an area under the curve of 0.96. Using the Paige Prostate 1.0 operating point, it achieved a sensitivity of 91% and a specificity of 94%, corresponding to the correct identification of challenging treated morphology in 59/64 slides using expert pathologists as the reference. False negative cases were typically represented by atypical small acinar proliferation that required expert pathological consensus confirmation. Conclusions: To our knowledge, this is the first AI based evaluation of residual disease in PC with hormone neoadjuvant therapy. Paige Prostate 1.0 effectively identified tumor despite treatment effects. Future work will include optimization of Paige Prostate 1.0 by training with RP specimens from a larger cohort of appropriate samples, as well as precise measurement of residual tumor burden to further improve its accuracy and reproducibility. Paige prostate residual disease detection 1.0 has the potential to impact emerging clinical practice at the patient level and to complement the pathological assessment of RPs in global phase 3 clinical trials, such as PROTEUS, in a standardized, reproducible, and robust way.