Ethan Bo

Ethan Bo’s Memento

My projects during the 2024-2025 school year in the Programming/Robotics 8 class

Twin Python Projects

I have whipped up two trinket projects in Python for my twin Python projects at the start of the year.

The word project was a “choose your adventure story” with one ending with randomely generated scenarios.

Link to the word project (“choose” your own adventure)

The number project was a number guessing game again, featuring using random statements.

Link to the number project (Number Guessing Game)

November Project

I decided to model a container ship on Tinkercad for my November project. It is on Tinkercad.

Link to the model on Tinkercad

Month of Failure Projects

For my month of failure project, I coded a blackjack game. It featured basic functions.

Link to my Blackjack game (month of failure project)

My 3 Technology Articles

Portable MRI:Bringing Advanced Imaging to the Patient’s Bedside

During a lesson, I found myself wondering if it is possible to develop a portable MRI machine that could scan patients without moving them from their beds. Surprisingly, this technology is already a reality and already transforming patient care by delivering bedside imaging quickly, enhancing efficiency, and improving outcomes. This transformative technology, able to deliver advanced imaging directly to the patient’s bedside, will revolutionize accessibility and efficiency in critical care.

A non-surgical medical imaging method called magnetic resonance imaging (MRI) generates three-dimensional images of the body’s internal organs. A computer can measure the signals that are released when atoms reorient after creating a powerful magnetic field, lining up the atoms in the body, and sending radio waves. MRI gives high-contrast images without the use of ionizing radiation, and it is an important diagnostic method for medical conditions like tumours and neurological disorders. In conventional magnetic resonance imaging (MRI) scanners, patients recline on a flat table within a large, clattering, doughnut-shaped device that creates a magnetic field using electrified coils, with scans typically lasting from thirty minutes to two hours. Open MRIs, which utilize two fixed magnets to minimize feelings of claustrophobia, may have poorer-than-desirable image quality.

“If somebody is really sick, they can’t be moved in a hospital. So we can look at that person,”-Jonathan Rothburg, founder of Hyperfine (Portable MRI Company)

Though they utilize smaller designs to render them portable and use weaker-strength magnetic fields, the same principles are utilized by portable MRI scanners. Such scans that can be completed in a maximum of fifteen minutes and with instant results are especially useful for bedside scanning in emergency settings, even though the image resolution may be sacrificed.

For those patients with serious illness who cannot afford to undergo normal scanning procedures due to a shortage of time and require immediate treatment, these portable devices enable MRI image scanning without requiring extensive tests. They are relatively far less expensive, provide a more comfortable experience, and do not require specialized shielding to function in various environments. Portable MRIs are highly portable and flexible, with imaging software compatible with an iPad.

AI in Radiology:Transforming Medical Imaging

By integrating advanced learning algorithms that enhance diagnostic accuracy, streamline workflow efficiency, and support personalized patient care, artificial intelligence can reshape medical imaging across various clinical settings.

Workflow. Structuring tasks to achieve a goal may be an intricate process. Yet with artificial intelligence assisting doctors in this mundane task, these lifesaving workers have an additional time window to provide care to a wider scope of people. Achieved by automating repeated tasks such as report generation, protocol selection, and case prioritization, this reduction to the workload will result in a more efficient environment. This strategy has already decreased chest X-ray interpretation times from around eleven days to three days, as described on the National Library of Medicine’s website.

The Feinstein Institutes for Medical Research, part of Northwell Health, have recently developed a way to use AI to detect cancer from reports obtained from MRI and CT scans, specifically pancreatic cancer, more quickly and accurately pointing out potential hazards to the patient.

“Too many cancers, especially pancreatic cancer, go undetected until it’s too late”-iNav on Northwell’s Website

Seventy to eighty percent of patients with pancreatic cancer are only diagnosed after the cancer has spread. That’s seventy to eighty percent of pancreatic cancer patients finding out about a disease that will likely kill them. Yet there is a way to detect the tumour earlier, and that way is iNav. This invention makes use of artificial intelligence to detect cancer early through analysis of the numerous images radiologists have to go through. These tools particularly excel at recognizing patterns and noticing tiny details that doctors may not be able to identify. Along with the tendency of cancer to go unnoticed until its late, dangerous stages, iNav was also used to detect conditions such as kidney cysts, as stated on their website.

Personal treatment plans in administering the remedies of cancer matter. Treatment styles and strengths will have to be calculated uniquely for every patient. Artificial intelligence aids in creating optimal treatment plans and accurately segmenting organs, tissues, and tumours, which improves precision in the administration of medicine, reducing damage to healthy tissue. Identifying genetic and molecular profiles for tailored therapies and reducing side effects are streamlined as well. Features from medical images extracted by artificial intelligence, such as tumour characteristics, aid prognosis and treatment planning. Prediction of responses to therapies based on radiomic profiles enhances personalized care.

Data privacy and ethical concerns are challenges against the clear benefits of artificial intelligence. Yet the integration of this technology into existing systems will be difficult. Continued investment in this solution, development of evolved ethical guidelines, and professional training to ensure patient-centred development can smoothen the transition. The rapid evolution of this innovation will require further research to refine these applications.

Artificial intelligence is already transforming medical imaging in radiology by enhancing diagnostic accuracy, streamlining workflow efficiency, and supporting personalized patient care. It has the potential to revolutionize clinical practices, with positive implications for patient outcomes and delivery of therapeutics.

Photon-Counting CT: The Cutting Edge of Medical Imaging

Medical imaging, including computed tomography (CT), is a foundational part of modern diagnostics, enabling the non-invasive visualization of internal structures. Photon-counting CT will act as a significant leap forward in radiology, increasing image quality, reducing radiation exposure, and enhancing diagnostic capabilities. Since its clinical introduction in 2021 by corporations similar to Siemens Healthineers, this innovation is assured to transform patient care.

CT scanners have evolved from single-section to multidetector systems since the 1970s. These upgraded scanners boast rapid, high-resolution imaging, using energy-integrating detectors. These detectors convert X-rays into light and then electrical signals, which result in the loss of some data in the process. Photon-counting CT instead uses detectors that directly count individual X-ray photons, preserving more information and enabling clear, precise images.

Photon-counting CT employs advanced detectors, usually manufactured from materials like cadmium telluride, to convert X-rays into electrical signals. By counting individual photons, electronic noise emitted is eliminated. This technology measures photon energy, allowing for tissue differentiation. Smaller detector sub-pixels can result in sharper images, such as the ones typically included in photon-counting CT. A lower radiation dose can be a result of improving dose efficiency. This helps pediatric patients and those who require frequent scans.

Photon-counting CT can be used in identifying cancerous versus benign masses, which can reduce follow-up scans. It can also be used in coronary artery and stent imaging, as it visualizes stent struts clearly. Brain imaging for strokes and hemorrhages is enacted with a 20% improvement in detecting small hemorrhages with photon-counting CT.

Yet this technology can not be without challenges. Photon-counting CTs cost a large amount, as the advanced detectors and systems are expensive, practically limiting access to this innovation to well-funded institutions. As of 2025, only a few hospitals have adopted this technology due to its limited availability. Further research is needed to confirm this technology’s optimal cell size and sensitivity and to confirm its efficacy.

This groundbreaking advancement will offer safer, more precise scans. While challenges like cost and availability persist, ongoing research will likely make this technology more accessible. Photon-counting CT, as it integrates into clinical practice, can redefine radiology, improving patient outcomes.

NHL Predictions

Along the Spring season, I did my NHL predictions chart. I used spreadsheet knowledge and additionally added a auto updating average points per game counter that would evolve along the season.