AI-based diagnostic pathology is a new and exciting field where computer programs and traditional tissue examination come together, starting a new chapter in the way we diagnose diseases. By employing machine learning and deep learning techniques, AI-enhanced systems can parse through vast amounts of histological data at unprecedented speeds, identifying patterns and anomalies often imperceptible to the human eye. Such systems not only augment the accuracy and consistency of pathological diagnoses but also expedite the processing of large-scale samples, reducing the turnaround times in clinical settings. The incorporation of AI into pathology has the potential to significantly reduce diagnostic errors, enhance prognostic accuracy, and facilitate more precise therapeutic decision-making, ultimately optimizing patient outcomes in the realm of precision medicine.
Key Publications:
- Rakaee M, Andersen S, Giannikou K, Paulsen EE, Kilvaer TK, Busund LTR, Berg T, Richardsen E, Lombardi AP, Adib E, Pedersen MI, Tafavvoghi M, Wahl SGF, Petersen RH, Bondgaard AL, Yde CW, Baudet C, Licht P, Lund-Iversen M, Grønberg BH, Fjellbirkeland L, Helland Å, Pøhl M, Kwiatkowski DJ, Donnem T. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Annals of Oncology 2023 DOI
- Rakaee M, Adib E, Ricciuti B, Sholl LM, Shi W, Alessi J V., Cortellini A, Fulgenzi CAM, Viola P, Pinato DJ, Hashemi S, Bahce I, Houda I, Ulas EB, Radonic T, Väyrynen JP, Richardsen E, Jamaly S, Andersen S, Donnem T, Awad MM, Kwiatkowski DJ. Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC. JAMA Oncology 2022 DOI
- Shvetsov N, Grønnesby M, Pedersen E, Møllersen K, Busund LTR, Schwienbacher R, Bongo LA, Kilvaer TK. A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images. Cancers 2022 DOI