key: cord-0939288-ucof5fd9 authors: Shabestri, Behrouz; Anastasio, Mark A.; Fei, Baowei; Leblond, Frédéric title: Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics date: 2021-05-10 journal: J Biomed Opt DOI: 10.1117/1.jbo.26.5.052901 sha: 5c44c68c9ac017158c8d76229638c9c570eb284f doc_id: 939288 cord_uid: ucof5fd9 Guest editors Behrouz Shabestri, Mark Anastasio, Baowei Fei, and Frédéric Leblond provide an overview of the JBO Special Series on Artificial Intelligence Machine Learning in Biomedical Optics. Most recently, AI methods are proving to be invaluable for a variety of tasks related to the detection and management of COVID-19. [24] [25] [26] Combining AI with optical breathalyzers may yield a rapid and accurate test for COVID-19, which is currently lacking and greatly needed. This JBO special series brings together late breaking research that describe the use of artificial intelligence in biophotonic applications, with an emphasis on ML and DL approaches. The series highlights the important role that ML and DL methods are playing in accelerating the development of innovative biophotonic technologies. This series is timely, for it comes as a growing number of the biomedical optics scientific community are starting to tackle the multiple challenges associated with the responsible adoption of AI methods. Issues such as robustness, reliability, and interpretability remain largely unaddressed but are critical for safe and effective deployment of AI-enabled biophotonic imaging and sensing systems. We hope you enjoy this special series, which includes the following twelve articles: Frédéric Leblond is a professor in the Department of Engineering Physics at Polytechnique Montréal, where he heads the Optical Radiology Laboratory. He works mainly in biomedical optics (including diffuse optics and spectroscopy), designing new surgical and pathology methods, enhancing medical imaging, and studying light propagation in biological tissues. He is the co-founder and was-until 2020-technical director of ODS Medical Inc., which is tasked with commercialization of his Raman-spectroscopy-based cancer-cell detection device. He is currently working with a number of industrial partners on development of several medical imaging techniques, fiber optical systems, and software. He also holds several patents. As his work also involves human subjects and is greatly useful to medical personnel, he has collaborative projects with many hospitals across North America. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging Learned sensing: jointly optimized microscope hardware for accurate image classification Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction Deep-learning based, automated segmentation of macular edema in optical coherence tomography Hyperspectral imaging for the detection of glioblastoma tumor cells in H&E slides using convolutional neural networks Hyperspectral imaging of head and neck squamous cell carcinoma for cancer margin detection in surgical specimens from 102 patients using deep learning Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks Deep learning-based framework for in vivo identification of glioblastoma tumor using hyperspectral images of human brain Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer Quantitative diagnosis of cervical neoplasia using fluorescence lifetime imaging on haematoxylin and eosin stained tissue sections All-digital histopathology by infrared-optical hybrid microscopy Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments Head and neck cancer detection in digitized whole-slide histology using convolutional neural networks Burn wound classification model using spatial frequency-domain imaging and machine learning Application of multiphoton imaging and machine learning to lymphedema tissue analysis Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning Artificial intelligence in multiphoton tomography: atopic dermatitis diagnosis Feature engineering applied to intraoperative in vivo Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: a diagnostic accuracy case-control study with multicohort validation Generating retinal flow maps from structural optical coherence tomography with artificial intelligence A deep learning system for automated angle-closure detection in anterior segment optical coherence tomography images Fully automated detection and quantification of macular fluid in OCT using deep learning Artificial intelligence (AI) applications for COVID-19 pandemic Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19 COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections Behrouz Shabestari is the acting director of the Division of Health Informatics Technologies and director of the NIBIB National Technology Centers Program. He directs the NIBIB programs in optical imaging and spectroscopy, and for x-ray, electron, ion beam, and computed tomography (CT)