GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK

Skeleton/bone marrow involvement in patients with newly diagnosed Hodgkin’s lymphoma (HL) is an important predictor of adverse outcomes1. Studies show that FDG-PET/CT upstages patients with uni- or multifocal skeleton/bone marrow uptake (BMU) when iliac crest bone marrow biopsy fails to find evidence of histology-proven involvement. The general recommendation is, therefore, that bone marrow biopsy can be avoided when FDG-PET/CT is performed at staging.

Our aim was to develop an AI-based method for the detection of focal skeleton/BMU and quantification of diffuse BMU in patients with HL undergoing staging with FDG-PET/CT. The output of the AI-based method in a separate test set was compared to the image interpretation of ten physicians from different hospitals. Finally, the AI-based quantification of diffuse BMU was compared to manual quantification.

Artificial intelligence-based classification

A convolutional neural network (CNN) was used to segment the skeletal anatomy11. Based on this CNN, the bone marrow was defined by excluding the edges from each individual bone; more precisely, 7 mm was excluded from the humeri and femora, 5 mm was excluded from the vertebrae and hip bones, and 3 mm was excluded from the remaining bones.

Focal skeleton/bone marrow uptake

The basic idea behind our approach is that the distribution of non-focal BMU has a light tail and most pixels will have an uptake reasonably close to the average. There will still be variations between different bones. Most importantly, we found that certain bones were much more likely to have diffuse BMU than others. Hence, we cannot use the same threshold for focal uptake in all bones. At the other end, treating each bone individually is too susceptible to noise. As a compromise, we chose to divide the bones into two groups:

• “spine”—defined as the vertebrae, sacrum, and coccyx as well as regions in the hip bones within 50 mm from these locations, i.e., including the sacroiliac joints.

• “other bones”—defined as the humeri, scapulae, clavicles, ribs, sternum, femora, and the remaining parts of the hip bones.

For each group, the focal standardized uptake values (SUVs) were quantified using the following steps:

1. Threshold computation. A threshold (THR) was computed using the mean and standard deviation (SD) of the SUV inside the bone marrow. The threshold was set to
   ���=SUVmean+2SD.

• 2. Abnormal bone region. The abnormal bone region was defined in the following way:

  Only the pixels segmented as bone and where SUV > THR were considered. To reduce the issues of PET/CT misalignment and spill over, a watershed transform was used to assign each of these pixels to a local maximum in the PET image. If this maximum was outside the bone mask, the uptake was assumed to be leaking into the bone from other tissues and was removed. Finally, uptake regions smaller than 0.1 mL were removed.

• 3.Abnormal bone SUV quantification. The mean squared abnormal uptake (MSAU) was first calculated as
  MSAU=meanof(SUV−THR)2overtheabnormalboneregion.

This calculation leads to two TSAU values; one for the “spine” and one for the “other bones”. As the TSAU value can be nonzero even for patients without focal uptake, cut-off values were tuned using the training cohort. The AI method was adjusted in the training group to have a positive predictive value of 65% and a negative predictive value of 98%. For the “spine”, a cut-off of 0.5 was used, and for the “other bones”, a cut-off of 3.0 was used. If one of the TSAU values was higher than the corresponding cut-off, the patient was considered to have focal uptake.

Results

Two scientific leaps,  in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.

Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.

Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.

Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.

While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus

read the original article which speaks about a lot more at https://www.axios.com/ai-machine-learning-biology-drug-development-b51d18f1-7487-400e-8e33-e6b72bd5cfad.html