Purpose/Hypothesis: When a bone fracture is suspected, physical therapists rely on clinical prediction rules and special-tests to determine the appropriateness of rehabilitation or the need for outside referral for diagnostic testing. While no one specialized test administered by clinicians can definitively diagnose a fracture, it has been shown that specialized tests in combination with others significantly strengthens the likely-hood ratio in ruling in/out the diagnosis. Diagnostic imaging such as x-rays, medical resonance imaging (MRI), computerized tomographic (CT) scan, and ultra sound (US) are costly. Healthcare practitioners, patients, and society as a whole may benefit if the healthcare provider can predict with better accuracy/confidence if a diagnostic test is needed. Financial benefits include decreased out of pocket expenses for the patient. Other benefits include decease in overutilization of unnecessary healthcare services, and exposure to radiation. The purpose of this preliminary phase of the study was to, (1) investigate if an iPhone speaker and microphone is sensitive enough to detect and graph a measurable change when generating sound frequency to detect a bone fracture via utilization of ebony wood, and (2) investigate the reliability of an iPhone to measure sound frequency on human cadaver bone. Number of Subjects: 7 Materials and Methods: The study was conducted in two phases. The first phase consisted of measurements taken on ebony wood, which has a similar density to human bone (Naylar, 2014) (chart has been removed) The measurements were taken utilizing an iPhone 7S application, LARSA Analyzer. The application functions by generating a sound signal through the iPhone speakers at a specific frequency range. Once the signal is generated, the microphone on the iPhone picks up the signal and generates a graph on the application. The first set of measurements were taken on a piece of ebony wood that is of similar size and density of an average adult human tibia. A fracture was then created and the measurements were repeated. A comparison of repeated measures was documented and analyzed (Table B). The second phase of the study consisted of utilizing the LARSA application on the iPhone to record signal on human cadaver tibia bone. The second phase of this study took place at the gross anatomy laboratory at Touro College School of Health Sciences, Bay Shore NY. Subjects consisted of seven (7) cadavers (5 males, 2 females) between the ages of 62 and 95 years old. The subjects were donated to the gross anatomy laboratory at Touro College School of Health Sciences, Bay Shore NY. Each subject was assigned number one (1) through (7), which was be the reference during the investigation. Results: The first phase of the study demonstrated a consistent graphed pattern on the pre-fracture and post-fracture phase on the ebony wood for all of the samples. The second phase of the study also showed similar results in demonstrating a consistent graphed pattern between the seven subjects. Peaks were detected and graphs at various points consistently for each subject. Conclusions: This study concludes that an iPhone microphone is sensitive enough to detect and graph a measurable change when generating sound frequency to detect a fracture created in materials that have similar density to human bone. Additionally, an iPhone may be a reliable and valid tool for physical therapist to utilize when a fracture is suspected. Clinical Relevance: The use of a smartphone device may be a reliable and beneficial tool for physical therapists when utilize in ruling in/out a bone fracture, or when determining the appropriateness of rehabilitation or the need for outside referrals.