According to the World Health Organization [1], cancer is the leading cause of death globally, with nearly 10 million deaths reported in 2020. Each year, around 400,000 children are diagnosed with cancer. The most common types of cancer varying by region.Laryngeal cancer is a type of head and neck cancer. According to Cancer.net, over 184,000 cases of laryngeal cancer were diagnosed worldwide in 2020 [3]. While it may not be as prevalent as breast, lung, or prostate cancer, laryngeal cancer presents unique opportunities for early diagnosis using audio and image data. Approximately 60% of laryngeal cancers are found in the glottis, and about 35% are in the supraglottis. For cancers confined to the larynx, the five-year survival rates are 84% for glottis cancers and 61% for supraglottis cancers [3]. Figure 1, provided by the American Society of Clinical Oncology, illustrates the medical anatomy of the glottis and supraglottis in the human throat.

Image reconstruction is the manipulation of digitized information obtained during body imaging into interpretable pictures that represent anatomical details and diseases. It is a vital step in producing visual results from a Computed Tomography (CT), Magnetic Image Resonance (MRI) etc. scanning devices. In all modalities (CT, MRI etc.) the data is acquired in form of slices or projections. In simple terms, the process of putting the projections together in a two-dimensional (2D) or three dimensional (3D) scans can be called image reconstruction. Back projection and forward projections are two commonly used operations in the image reconstruction pipeline. And since the image reconstruction algorithms can be compute intensive, there is desire to fasten the processing time for it, to get the final scan faster. The scans are what a medical professional such as a radiologist use to provide diagnosis to a patient, hence faster access to scan can result in more timely diagnosis.  With the goal to reduce the processing time for image reconstruction pipeline, the authors worked to analyze and optimize representative back projection and forward projection algorithms. This paper provides details on the various optimization and code migration work that was done to achieve targeted performance metrics using a hardware vendor neutral programming language.

This paper provides a short overview of how artificial intelligence (AI) can be used on voice data for early detection of various medical conditions, thus accelerating triaging and improving patient outcome.Clinical Relevance — Worldwide more than 10 million people suffer from Parkinson’s disease, 5% of adults (~280 million) suffer from depression and more than 184000 laryngeal cancer diagnosis were made in 2020. All these diseases and many more can be potentially detected in earlier phases by using AI on voice, hence giving medical professionals an AI-augmented way to address such conditions in early stages.