AI algorithm can help in target identification in an emergency — A Golden Hour opportunity

AI algorithm can help in target identification in an emergency — A Golden Hour opportunity

Let’s begin by discussing Traumatic Brain Injury or TBI, and then move on to the importance of the “golden hour” in such scenarios. The opening is very abrupt and the second line is not relevant to it.

In emergency response, every passing moment is critical, and the initial phase of following an incident is often referred to as the “Golden Hour” – a crucial window of opportunity for timely intervention and enhanced chances of positive outcomes. Amid the pandemic, artificial intelligence (AI) algorithms become a ground-breaking tool for quickly identifying emergency targets. These algorithms use sophisticated machine learning, data analysis, and pattern recognition skills to extract important information from the chaos quickly and precisely.

Brain Trauma Cases In Emergency/Casualty

A head injury is defined as a traumatic brain injury (TBI) that results in a disruption or cessation of brain function, either with or without interstitial hemorrhage. These frequently results in complex health issues because of the lengthy recovery and care periods, which raise costs and cause issues with social and economic stability, among other things.

For those between the ages of 1 and 44, injuries are the leading cause of death. In 2020, there were 278,345 trauma-related deaths [6] in the US, with over 70% being unintentional. India has the highest annual rate of head injury globally, with more than 1 million serious head trauma, and 60 percent of all TBI caused by road traffic accidents. In the country, statistics from Indian Head Injury Foundation in the year 2020 shows 38 to 43 percent mortality rate in severe TBI. [7]

Patients who sustain severe injuries but may not necessarily have life-threatening consequences are best served by receiving care in hospitals with special staffing and protocols that are certified as trauma centers. While state-specific criteria for this classification (and whether transport is required to reach them) differ, most follow the American College of Surgeons Committee on Trauma standards.

Category	Statistics
Global Incidence	Over 69 million people experience TBIs annually [1]
Leading Causes	Falls, accidents, sports-related injuries, assaults
Morbidity Rates	Approximately 2.8 million TBI-related hospitalizations globally [2]
Mortality Rates	TBI contributes to 30% of all injury-related deaths [3]
Age Groups Affected	Highest incidence among children and the elderly
Emergency Room Cases	TBI-related ER visits in the millions annually [4]
AI Impact	AI-assisted diagnostics improve accuracy by 20-30% [5]

Diagnostic criteria including Golden Hour

The Golden Hour is a narrow timeframe following a traumatic event during which immediate medical intervention significantly improves outcomes. For traumatic brain injuries (TBIs), the diagnostic criteria within this crucial window involve a comprehensive assessment of clinical signs, symptoms, and advanced imaging techniques.

Diagnostic criteria play a pivotal role in the timely and accurate identification of medical conditions, and the “Golden Hour” concept underscores the critical importance of swift diagnosis, particularly in emergencies. The Golden Hour is a narrow timeframe following a traumatic event during which immediate medical intervention significantly improves outcomes. For traumatic brain injuries (TBIs), the diagnostic criteria within this crucial window involve a comprehensive assessment of clinical signs, symptoms, and advanced imaging techniques.

Source: Adapted from the Figure 1 on Multimodal analysis of Neuro-ICU data with AI-ML^[8] 

In the initial moments after an incident, healthcare professionals focus on rapid primary assessment, ensuring the patient’s airway, breathing, and circulation are stable. Once stabilized, a targeted neurological examination is conducted, assessing factors such as level of consciousness, motor responses, and pupil reactions. Diagnostic imaging, including computed tomography (CT) scans, becomes instrumental in identifying the presence, location, and severity of brain trauma.

Time is of the essence in adhering to the Golden Hour principles, as delays in diagnosis and intervention may exacerbate the injury and impede recovery. Integrating artificial intelligence (AI) algorithms further enhances diagnostic speed and accuracy, aiding healthcare providers in swiftly interpreting imaging results and making informed decisions. By aligning diagnostic criteria with the Golden Hour imperative, medical professionals can maximize their capacity to initiate life-saving interventions promptly and improve overall patient outcomes in emergency scenarios.

How Can AI And Machine Learning Help Identify Treatment Targets?

In terms of emergency medicine, the integration of Artificial Intelligence (AI) and Machine Learning (ML) presents an opportunity for detection of targets such as midline shift (MLS), intracerebral hemorrhage (ICH), or fractures and thus quicker treatments. Here’s how AI and ML contribute to identifying treatment targets within this crucial time frame:

Rapid Image Analysis:AI algorithms excel in analyzing medical imaging data, including CT scans and MRIs, with unprecedented speed and accuracy. Within minutes, these algorithms can assess the presence and characteristics of MLS or ICH, aiding clinicians in swiftly identifying pathologies, acute effects, and prognosis.
	Pattern Recognition:Machine Learning algorithms are trained on vast datasets, enabling them to recognize subtle patterns indicative of specific injuries. This capability is particularly valuable for identifying MLS or ICH in imaging data, providing healthcare professionals with early insights crucial for treatment decisions.
Clinical Decision Support:AI is a powerful clinical decision-support tool that offers real-time recommendations based on data analysis. In the context of MLS or ICH within the Golden Hour, AI can assist clinicians by providing insights into the severity, location, and potential treatment options, facilitating informed decision-making.
	Integration with Electronic Health Records (EHR):AI can integrate seamlessly with Electronic Health Records, consolidating patient data for a comprehensive analysis. This integration enables a holistic understanding of the patient’s medical history, identifying pre-existing conditions or factors that may influence treatment strategies.
Alerts and Prioritization:AI systems can be designed to generate alerts and prioritize cases based on the presenceurgency and severity of MLS or ICH. This ensures that healthcare providers can focus on cases requiring immediate intervention, aligning with the time-sensitive nature of the Golden Hour.
	Continuous Monitoring and Feedback:Machine Learning models can continuously learn and adapt, providing ongoing feedback to enhance diagnostic accuracy. This iterative learning process ensures that the AI effectively identifies treatment targets, contributing to improved patient outcomes within the Golden Hour.

The above table is good, but can we shorten/edit the sentences? The first 2 blocks have repetative information in their second line. 

Advantages of Using AI in Emergencies

Integrating Artificial Intelligence (AI) in the medical field, especially during emergencies, brings forth many advantages that significantly contribute to efficiency and quick prognosis. Advantages using AI in emergencies – at a glance:

1. Swift Image Analysis:
   • AI algorithms excel in rapid image analysis, allowing for quick interpretation of medical imaging data such as CT scans or MRIs. This speed is paramount in emergencies, providing healthcare professionals with immediate insights into the patient’s condition.
2. Automated Diagnostics:
   • AI facilitates automated diagnostics by quickly identifying patterns and abnormalities in medical images. This automation expedites the diagnostic process, enabling healthcare providers to make informed decisions promptly.
3. Early Detection and Intervention:
   • AI’s ability to detect subtle patterns and anomalies in medical data enables early identification of conditions. In emergencies, early detection is crucial for initiating timely interventions, aligning with the concept of the Golden Hour, and improving patient outcomes.
4. Personalized Treatment Plans:
   • AI analyzes vast datasets, including patient records and treatment outcomes, to recommend personalized treatment plans. This individualized approach enhances efficiency by tailoring interventions to each patient’s specific needs, optimizing the chances of a successful prognosis.
5. Predictive Analytics:
   • AI utilizes predictive analytics to forecast potential outcomes based on historical data. This proactive approach allows healthcare providers to anticipate complications or deterioration in a patient’s condition, enabling preemptive actions for a better prognosis.
6. Continuous Monitoring:
   • AI supports continuous monitoring of patients, providing real-time updates on vital signs and changes in health status. This constant surveillance ensures quick responses to any deviations from the expected trajectory, enhancing the efficiency of patient care.

Conclusion

Integrating Artificial Intelligence (AI) in emergency medical scenarios marks a paradigm shift, offering unprecedented efficiency and rapid prognosis advantages. AI’s prowess in swift image analysis, automated diagnostics, and personalized treatment plans aligns seamlessly with the critical timeframe of the Golden Hour. As we embrace these technological advancements, it becomes evident that AI not only enhances the speed and accuracy of medical decision-making but also holds the potential to revolutionize emergency care, ushering in a new era of timely and effective interventions.

There is no mention of the NeuroShield: CT tool in this document. Very little coverage of what TBI is, and what are its consequences. We can also mention companies which are into Automated detection of such targets in TBI cases, I have mentioned a few in my articles. Also need to cover which doctors would be benefitted by such automated emergency tool, radiologists, neurologists, neurosurgeons. What modalities are used for diagnosis of such condition? CT is the first choice because diagnosis should be done quickly. And Image analysis, pattern recognition stuff is done using CT, XRAys, MRIs. 

References: 

1. World Health Organization (WHO) https://www.who.int/publications-detail-redirect/rehabilitation-for-persons-with-traumatic-brain-injury
2. Centers for Disease Control and Prevention (CDC) https://www.cdc.gov/traumaticbraininjury/get_the_facts.html
3. National Center for Injury Prevention and Control https://www.cdc.gov/about/leadership/leaders/ncipc.html
4. Emergency Room Visits for Traumatic Brain Injury (CDC) https://www.cdc.gov/mmwr/volumes/66/ss/ss6609a1.htm
5. Ahuja AS. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019 Oct 4;7:e7702. doi: 10.7717/peerj.7702. PMID: 31592346; PMCID: PMC6779111. 
6. Jordan J, Approach to the Trauma Patient. MSD Manual. NJ, USA. Mod. Sep 2022. Available at: https://www.msdmanuals.com/professional/injuries-poisoning/approach-to-the-trauma-patient/approach-to-the-trauma-patient 
7. https://indianheadinjuryfoundation.org/traumatic-brain-injury/
8. Cascella M, Montomoli J, Bellini V, Vittori A, Biancuzzi H, Dal Mas F, Bignami EG. Crossing the AI Chasm in Neurocritical Care. Computers. 2023; 12(4):83. https://doi.org/10.3390/computers12040083