In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany’s and Srinivasan’s ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists.

A series of experiments has been conducted with probes made from silicon, glass, tungsten and polyimide within a developed brain phantom, and the insertion behavior, forces and dimpling are compared to in vitro and in vivo models. This allows the choice of proper insertion parameters and probe structure to reach a compromise between needle stability and tissue trauma as a result of insertion. According to the performed experiments, the reduced interfacial area between the needle tip and the brain will result in reduced insertion force. High insertion speed (100 mm/min) reduces the dimpling but not the penetration force necessarily. In vivo insertion and retraction of the fragile probes made from silicon is possible without pia and/or dura removal.

A fiber optic probe was developed for guidance during stereotactic brain biopsy procedures to target tumor tissue and reduce the risk of hemorrhage. The probe was connected to a setup for the measurement of 5-aminolevulinic acid (5-ALA) induced fluorescence and microvascular blood flow. Along three stereotactic trajectories, fluorescence (n = 109) and laser Doppler flowmetry (LDF) (n = 144) measurements were done in millimeter increments. The recorded signals were compared to histopathology and radiology images. The median ratio of protoporphyrin IX (PpIX) fluorescence and autofluorescence (AF) in the tumor was considerably higher than the marginal zone (17.3 vs 0.9). The blood flow showed two high spots (3%) in total. The proposed setup allows simultaneous and real-time detection of tumor tissue and microvascular blood flow for tracking the vessels.