Treatment Planning - Science topic

Hello Mandava Prasad

Artificial Intelligence (AI) is undoubtedly making significant strides across many disciplines, including medicine, dentistry, and specifically in orthodontics and dentofacial orthopedics. AI tools can now assist in image analysis (e.g., cephalometric tracing, panoramic X-ray interpretation), growth prediction, malocclusion classification, and even preliminary treatment planning. However, relying solely on AI for diagnosis and treatment planning would be premature and potentially risky.

While AI excels in pattern recognition and data-driven decision support, it has limitations—especially in areas requiring human insight and clinical judgment. Key aspects of orthodontic diagnosis, such as:

...are still deeply reliant on the clinician's expertise, empathy, and in-person interaction.

AI should be viewed as an adjunct tool—not a replacement for clinical decision-making. When used properly, it can enhance accuracy, reduce workload, and support evidence-based practice. However, the final diagnosis and treatment planning must remain a clinician-led process, taking into account the patient’s unique needs, values, and social context.

In summary, AI can significantly support, but not replace, the orthodontist. Its reliability is high for specific technical tasks, but not for holistic, individualized care. The ideal future lies in human-AI collaboration, where AI enhances the clinician's ability to deliver precise and personalized treatment.

In practical medicine, artificial intelligence facilitates diagnostics and treatment of dental pathologies and has a lot of advantages. However, when using such technology, problems may arise, including ethical ones. Despite all the achievements, artificial intelligence doesn’t replace the expertise, judgment, and patient care that only a dentist can provide.

The need for data, since training neural networks requires a large amount of information about the norm and pathology, some clinics are in no hurry to share it.

Confidentiality, that is, the protection of patient data.

High cost software.

The need to train personnel to work with artificial intelligence.

Ethical issues related to liability for AI errors, deterioration of interaction between doctor and patient, and the quality of care for those who do not have access to this technology may also decrease.

Thus, at present, artificial intelligence cannot completely replace the role of a dentist, since effective treatment of patients requires a combination of technical and interpersonal skills that cannot be completely replaced by a machine. In addition, the human factor is a very important aspect in the treatment of oral diseases, since the elimination of the problem is not limited to the technical side, but also to the psychological comfort of the patient and the establishment of an emotional connection between the doctor and the patient. In the future, artificial intelligence may become more developed and be able to perform more complex functions, but the need for a dentist will always remain to solve more complex and non-standard situations, as well as for diagnostic and therapeutic procedures that require maximum precision and elegance.

Generative AI has the potential to revolutionize healthcare by enhancing diagnostics, accelerating drug discovery, personalizing treatment plans, and improving patient care through data-driven insights. Its ability to analyze vast datasets, identify patterns, and assist in decision-making can lead to faster, more accurate diagnoses and innovative treatment approaches, ultimately improving patient outcomes and reducing healthcare costs. However, its integration also raises concerns about patient safety, ethical dilemmas, and data privacy. AI-driven decisions may lack human empathy and contextual judgment, leading to misdiagnosis, biased recommendations, or over-reliance on technology. Additionally, security risks, misinformation, and the potential displacement of healthcare professionals further complicate its adoption. To maximize benefits while minimizing risks, AI must be used as a tool to support, not replace, medical professionals, ensuring transparent algorithms, regulatory oversight, and ethical safeguards to protect patient safety and uphold trust in the healthcare system.

This is just a personal opinion:

Direct multiomics integration for "people" is very difficult. But "AI" has a good potential to do it effectively. What "people" can do is to train AI more efficiently. For this we need to significantly improve the quality of the data we acquire and feed to AI. It is particularly important for metabolomics data, but also there is room for improvement in genomics as well. We also need to have much larger sample size, longitudinal studies, and overall better designed experiments that will be more coordinated between multiple research groups. Realistically speaking it is a great challenge when working with people.

In one study involving the wellbeing of palliative care workers, we found that young and female respondents, those engaged in hospital based palliative care, having a poor work environment, facing recent unemployment, having less experience, working for more number of hours, and having more number of patients dying in the previous month, all had a lower level of wellbeing.

Full study:

Herbal remedies that have shown promise in preventing or treating urinary stones include Ziziphus lotus, Chanca piedra, Herniaria hirsute which are believed to assist in breaking down stones, and herbs like dandelion root and nettle leaf, known for their potential diuretic properties. However, it's crucial to consult with a healthcare professional before incorporating these into a treatment plan. The approach to incorporating phytotherapy into a comprehensive treatment plan should be guided by the specific type of urinary stone, with considerations for hydration, dietary adjustments, and medical advice tailored to individual cases.

Yes, there is an intellectual difference between machine learning and deep learning in the context of artificial intelligence models. While both machine learning and deep learning are subfields of AI and involve training models to make predictions or decisions, they differ in their approach, techniques, and level of complexity.

Machine Learning: Machine learning focuses on developing algorithms and models that can learn patterns and make predictions or decisions based on data. It involves the design and development of statistical and computational models that can automatically learn from data without being explicitly programmed. Machine learning models typically rely on feature engineering, where relevant features are manually selected or engineered from the input data. These models learn from the input-output pairs or examples in the training data to generalize and make predictions on new, unseen data.

Deep Learning: Deep learning is a subset of machine learning that specifically utilizes artificial neural networks with multiple layers, known as deep neural networks. Deep learning models are designed to automatically learn hierarchical representations of data through multiple layers of interconnected nodes or neurons. These models can extract complex patterns and features directly from the raw input data, eliminating the need for extensive manual feature engineering. Deep learning algorithms utilize techniques like backpropagation and gradient descent to iteratively update the weights of the neural network and optimize the model's performance.

The main difference between machine learning and deep learning lies in the complexity and capacity to learn intricate patterns. Deep learning models with their deep neural architectures can capture and learn complex relationships and representations in the data, making them particularly effective in tasks such as image recognition, natural language processing, and speech recognition. Machine learning, on the other hand, encompasses a broader range of algorithms, including traditional statistical methods, decision trees, support vector machines, and more, which are often used for a wide array of tasks and may require manual feature engineering.

new scan for injured@Greg Clary @Nino Chaushev @Dana W Moore @Morteza Saeidi @Maalika Smith @Rob Haaxma@César Caparó-Zamalloa@Victoria Marca@Pilar Mazzetti@Carlos Cosentino@Ricardo F Allegri@Walter De la Cruz@Indira Tirado-Hurtado@Carlos D'Giano@Danilo Sánchez Coronel@Mauricio Farez@Luis E. Torres@Sheila Castro-Suarez@María Julieta Russo@John Charles Steele@Mario R Cornejo-Olivas@Jaime Fandiño@Claudia Cejas@Oleg Borisovich Zhukov

I am not sure if there are apps that can specifically analyze the mini-esthetics but there are definitely apps that can perform the simulation and VTO.

Metal stent to drain the jaundice. If bilirubin comes down, then FOLFIRINOX chemo if fit, GemCap if not fit

Afnan As how about a study comparing Proton (MFUD) Vs Vmat in Prostate cancers , you will be surprised to note that proton have somewhat been inferior primarily because of increased toxicity to the femoral heads because in Proton THERAPY there is no Intensity Modulation like in photons it is more like a Multi Field Optimisation where two three or four single field optimised are applied together .

Dear Mohammad

take a look at Generative Adversarial Network, GANs have shown a great success in training and generating realistic-looking data. have a look at the following paper :

It is a very interested question Edurad

I think this depends on the experience in this field .Also the age,weight,length,and the causes of orthotic used may be effect on the wearable devices

CT may help you to give some indication .I think this question cane be answers correctly after different study with different process and materials using compressive wearable orthotic devices

I follow the question

Francisco is correct in his comments. I will add a few as well.

Calculating MU/time and calculating dose are the inverse of one another. You need to be given MU to calculate dose, or you need prescribed dose to calculate MU. Typically one is tasked with calculating MU or time after the dose is prescribed by the Radiation Oncologist. In simple calculations the dose would be prescribed to a point in the body and the field apertures shaped to conform to the local anatomy, followed by normalizing to an isodose line that covers the desired area. All of this goes into the calculation of MU or time needed to get the prescribed dose. Modern techiniques involve the use of inverse planning utilizing arcs, dynamic MLC's, etc. It is always important to optimize the dose distribution, whether in linac based therapy or cobalt teletherapy. The ability to optimize the dose distribution is tied to the imaging available (2D, 3D, 4D, PET, MRI, etc.) the technology contained in the treatment planning system, and the skill of the planner.

100 ug of protein extract should be quite high concentration in this case when you have 1.8 ug/ul. It means, that you have to add at least 55.56 ul of your extract into one well to achieve 100 ug/well. And it means, that you will dilute your cell culture medium quiet a lot. Do you have in mind concentrations 100 ug/100 ul, 50ug/100 ul, etc. or something else? (100 ug/ml?)

Hello

the objective of lasers is to mark the position of the isocenter on the skin or on the contention of the patient who will be treated.

Practically, the CT scan is performed and then the physician delineate the target tumour on the TPS (treatment planning system). The isocenter is then put at the centroid of the delineated tumour, and the coordinates are exported to the laser system. Laser sources move automatically to the right position and the patient, who still is on the scanner couch, is tattooed at this precise location.

The aim is just to ensure that the patient will be placed at the same place under the LINAC than during virtual simulation.

This process has nothing to do with potential movement of the patient or of the tumour.

Such movements are managed using either dedicated imaging tools on the LINAC (2D or 3D) or respiratory management systems for tumours that naturally move (typically lungs, liver).

Part of the issue is framing the problem in a way that lends itself to parallelism, and that lends itself to attack by other types of hardware besides the CPU, like the integer and short float cores in a GPU.

No problem! If you have more questions, you can continue on this thread or write to me personally.

Salutations,

Bruno

hi

what is the 3d dose file format? its format is dicom?

i think you should convert both result ( rt , 3ddose files) to images (matrix) and thus change size of them.

Hi Falk!

Thank you so very much for your reply.

Very much appreciated.

Warm regards,

Lea

Dear you have a real complicated charcoal foot.  I can see the the strategy for managment is:

1- The apparent cause for the ulcer and deformity with consequent retarded healing is bone factor. so first please thurouhly  exclude other known 5 factors; as blood quality , quantity, associated morbidity (you can revise that here; http://onlinelibrary.wiley.com/doi/10.1111/jdi.12425/abstract

2- when corrected all other factors except the bone factor, inform and explain to the patient that this is a trial to avoid amputation is possible!! and have medico-legal concent that the procedures and operations you are going to do is for limb salvage otherwise amputation is the only way.

3- Prepare the foot and the wound by broad spectrum AB cover including anti Mersa

4- in the OR under fluoroscopic guide do excision of the small completely separated bone segments + try to correct the deformity even by chiselling part of the bone.  then if there is no more infection and the wound is clean, so under cover of antibiotics specially MERSA & anaerobes do fixation to the ankle.  

5- Then according to the situation or remaining bones,   fixation will be  ; by cannulated nails or external fixation 

6- daily care for the wound, supplement to bone matrix which is rarefied, continue antibiotics and you can allow patient to walk in the room for a month, till radiological evidence of starting bone healing and fixation

7- Inform the patient that this procedure can be repeated 

Best Regards

Hello Richard,

That's odd - running the margins postestimation example after etpoisson with the svy prefix works fine for me in Stata 14.1 (see help etpoisson documentation).

Anyway, instead of using the svy prefix you might consider using the pweight and vce(cluster) options when specifying etpoisson. This will yield exactly the same results and margins should work in any case.

Regards,

Alex

You are welcome Dr.Mahmoodzadeh,

I recommend breast conserving surgery and SLNB then chemotherapy. If there was indication for radiation ,it will be done after delivery.The main question (for me as a surgeon) is doing sentinel node in this pregnant woman or no .Based on data , SLNB with radiolabeled drug is more safe than blue dye in pregnancy . I would discuss possible risks to the patient and if she didn't agree for SLNB ,I will do 1st level sampling and check 2nd and 3rd level by palpation.

Hello there Alok, hope you are well. This question has a lot of divisions and we could see a long debate. lets hope the followers give their valuable comments and share their knowledge.

In my personal experience and as far as my knowledge is concerned regarding the subject, endo-perio or perio-endo whichever the case, we must perform endodontic therapy of a tooth. Curretage or root surface debridement is for sure recommended in case there is severe periodontal condition. Local irrigation would be a part of our overall treatment planning and maybe done. As far as crown fabrication is concerned, that would depend on the size of the access we make for endodontic therapy. and well extraction would be of course the BEST option for a tooth which is hopeless. 

I am attaching a link for you to have a look. Its an old article in dental update but quite sound and simple. Please share if you have something new,

Best wishes !

The care of the morbidly obese patient is embedded in my critical care course for BSN students. I treat it like any other risk factor for each disease process that we cover in my critical care course.  For example, when discussing myocardial infarction/cardiac arrest/cardiogenic shock, I talk about the increased risk to obese patients (increased demand on an impaired pump and decreased peripheral circulation).  I include discussion about why many patients in these higher risk groups are unable to maintain a cardiac diet and exercise regimen.  (Dependence upon others for meals, limited financial resources, physical debilitation, fear/anxiety/depression, etc.).  Therefore, the importance of setting realistic goals for each patient and involving caregivers is included.  I also discuss the use of specialty beds and lifts in maintaining skin integrity, mobility, and decreasing the risk of infection.  I discuss the need to match the physical ability of the nurse to perform CPR to the size of the patient he or she is attempting to compress.  (At 120 lbs., I often found it difficult to provide adequate compressions to obese patients, despite correct technique. I didn't have the strength or mass to be effective.  I make sure the students understand that asking for help with compressions provides better patient outcomes; it's about the patient not your pride).

Hi Christine,

In addition to the AA (Alcoholic Anonymous) model and the CBT (Cognitive-behavioral therapy) approaches to the treatment of substance-related disorders, there are strengths-based approaches such as motivational interviewing (MI) and solution-focused brief therapy (SFBT), etc. They attempt to elicit the client's motivation, cooperation, and strengths from the very beginning in the treatment process. Please see the following links.

Hello, Haroon!

It is quite complex maxillo facial rehabilitation of patients with major loss in this area, but dental implants are one of the best options for fixing these prostheses.

Best regards!

Even using positioning info per fraction can not give you an accurate daily DVH if you simply use the original planning CT scan. On a daily basis, the organs may distort slightly, contours change, functional units of the organs ar in different locations and thus get a different dose. So we are talking about ever better, but still approximations...which will depend greatly on the body site in question...

Sort for rectum and operate next week for both colon and rectum

crm not threatened, hence no need to wait for 8 weeks

good to rule out Hnpcc history

Two parameters that would be particularly useful are heart-rate variability and a-fib duration.  There are relatively simple algorithms that have been available for many years to capture and report these parameters.  If I can help further, please let me know.