Rule-Based Bilingual Decision-Support Prototype for Foot-and-Ankle Orthosis Selection Knowledge Representation and Preliminary Face-Validity Assessment
This paper presents Orthotica, a transparent, rule-based, bilingual decision-support prototype for foot-and-ankle orthosis selection, detailing its knowledge architecture and demonstrating preliminary face validity through high retrospective concordance with clinical prescriptions and expert ratings, while acknowledging the need for future independent validation.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Choosing the right brace for a foot or ankle is a complex puzzle that doctors and specialists solve every day. It requires weighing how strong a person's muscles are, how flexible their joints remain, and what kind of movement they need to walk or stand. The medical world has many different types of braces, ranging from simple soft supports to rigid devices that lock the ankle in place, and even specialized designs that help correct the way a person walks. However, the information needed to make these choices is scattered across countless reports, reviews, and manufacturer guides. There is no single, open tool that brings all this evidence together into one clear, checkable list. Without such a tool, the decision often relies heavily on the individual experience of the specialist, which can lead to inconsistencies.
A researcher named Mohamad Firas Wahbeh has built a digital prototype to help organize this scattered knowledge. He created a free, bilingual computer program called Orthotica that acts as a transparent guide for selecting foot and ankle braces. The program does not use complex, hidden algorithms that learn from data; instead, it follows a clear set of written rules based on existing medical literature. When a user enters details about a patient's condition, the software checks these rules to suggest the most appropriate type of brace. The goal was not to replace the doctor's judgment but to provide a structured way to ensure no important detail is overlooked. The program is designed to be used offline, meaning it works without an internet connection, and it is available in both English and Arabic to serve a wider range of users.
To test if this tool made sense, the researcher applied it to thirty real patient records from a clinic in Damascus, Syria. These records contained the original prescriptions written by a certified orthotist. When the software analyzed the same patient data, it matched the original category of the prescribed brace in every single case. In twenty-eight out of the thirty cases, it also suggested the exact same specific subtype of brace. This high level of agreement suggests the tool is logically sound, though the researcher notes that because the rules were partly shaped by conversations with clinicians from that same region, the results might reflect a shared way of thinking rather than a completely independent test. The tool also successfully flagged a case involving severe muscle stiffness where a standard brace might be unsafe, correctly prioritizing caution over a simple match.
The researchers also asked ten medical experts from the region to review the tool. These specialists, including orthotists and rehabilitation doctors, rated the program very highly for its usefulness and clarity, giving it an average score of nearly nine out of ten. They appreciated that the software explained its reasoning for every suggestion, allowing them to see exactly which rules led to a specific recommendation. However, the experts also pointed out that the numbers used to weigh different factors were assigned by the author based on informal input, rather than a formal, global consensus process. To address this, the researcher plans to organize a larger, international panel of experts to formally agree on these weights in the future.
A critical part of the study involved testing how sensitive the tool's suggestions were to small changes in those weights. The researchers simulated shifting the importance of various rules up and down by twenty percent to see if the top recommendation would change. They found that for the broad category of the brace, the suggestion remained stable in almost every instance. However, when looking at the specific subtypes of braces, the recommendation was more likely to shift if the weights changed slightly. This indicates that while the tool is robust for general guidance, the finer details of the prescription are still sensitive to how the rules are tuned. The study concludes that Orthotica is a valuable educational and organizational prototype that brings transparency to the decision-making process, but it is not yet a fully validated medical device ready to replace clinical judgment. It serves as a clear, inspectable starting point that invites further collaboration to refine its rules and prove its effectiveness in diverse real-world settings.
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