Source Name: Lumos

Lumos Health Accelerator Onboards Startups in Early Cancer Detection and Advanced Cancer Prognostics

Apr 12, 2021   06:46 AM 
New Delhi, Delhi, India

Lumos, a premier Healthcare speed scaling program, today announced the addition of Onward Assist and ErlySign to its speed scaling program. The startups were selected after a careful evaluation of their technological strengths and business capabilities. Lumos will provide clinical, technical and business advisory to help the startups scale and will offer infrastructure credits to help defray certain operating expenses.

 

Lumos is a speed scaling ecosystem that helps early-stage healthcare startups grow through timely and powerful business and technical advisory services. They have partnered with premier healthcare organizations to provide valuable mentoring, advisory and business development services to enable startups hit impressive growth trajectories. The existing repertoire of companies under Lumos are Kronikare, AyuRythm, Alixir, and Raybaby continue to grow with the numerous speed scaling capabilities offered by Lumos.

 

"Lumos continues to attract teams of high caliber and deep technical prowess into its speed scaling program," said Devang Mehta, Partner at Anthill Ventures and Lumos Founding Member. "Early cancer detection through sputum and digital pathology predicated on layers of AI/ML are tremendous growth areas and we look forward to leveraging our strengths in ensuring non-linear growth paths for these companies.”

 

"Over the course of the last few years my interactions with healthcare startups have been in-depth and have given many insights on what is needed in innovation in healthcare. The Lumos Heath program allows healthcare start-ups a truly unique experience to work with both healthcare experts and business experts to scale their business rapidly,” said Anjali Ajaikumar, Executive Director of Strategy at HCG and Lumos Founding Member. “Onward Health and ErlySign are the two newest startups to join our program and we are extremely excited to help them reach their fullest potential. They will join a portfolio of startups that are growing well in both the Indian and international markets."

 

About Onward Health

Onward Assist is an advanced cancer prognostics platform that helps Oncologists take better treatment decisions and improve outcomes. The company provides computer vision and ML-based automated diagnostic tools and treatment decision support tools used by Histopathologists, Radiologists, and Oncologists.

 

It leverages deep-tech to help localize cancer and reduce unnecessary biopsies. Onward Assist partners with clinicians to build machine learning models that run on images of new patients to automatically identify the location of the tumor, stroma, and combine data with other patient-related information to derive advanced insights on tumor classification and staging.

 

The company is engaged in continuous innovation and is associated with cancer institutes with its clinical collaborations focused on novel approaches, challenges faced in cancer prognostics, and important biomarkers.

 

About ErlySign

ErlySign develops non-invasive, early test kits for detecting oral cancer using saliva. Oral cancer is a major and growing global public health problem, and it remains a major cause of death worldwide. Historically, the death rate associated with oral cancer is particularly high due to late-stage diagnosis and intervention. Currently, the vast majority of patients are detected through a visual exam and/or are symptomatic, at which point they are likely late stage. As a result, oral cancer often goes undetected to the point of metastasizing.

 

ErlySign’s solution is an easy, accurate, affordable, and painless way for early detection of Oral Cancer. The kit gives detailed instructions starting with saliva collection to shipping of the sample for diagnosis. ErlySign’s simple and non-invasive detection technique is a promise to revolutionize the way oral cancer is being detected and treated in India and all over the world.