CTRI/2026/01/100865 [Registered on: 12/01/2026] Trial Registered Prospectively
Last Modified On:
09/06/2026
Post Graduate Thesis
No
Type of Trial
Observational
Type of Study
Follow Up Study
Study Design
Other
Public Title of Study
AI-Based Medical Image Analysis Software using X-ray Imaging.
Scientific Title of Study
An ambispective, multi-center, observational study to develop and validate AI Model to assist orthopaedic surgeons in the precise selection of sizes of nails, screws, and implants.
Trial Acronym
NIL
Secondary IDs if Any
Secondary ID
Identifier
NIL
NIL
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
Name
Dr Bhaumik Dave
Designation
General Manager
Affiliation
Nuvo AI Pvt. Ltd
Address
135/139 Bilakhia House Muktanand Marg Chala Vapi Pardi Valsad 396191 Gujarat
Valsad GUJARAT 396191 India
Phone
9978232094
Fax
Email
bhaumik.dave@nuvo.ai
Details of Contact Person Scientific Query
Name
Ms Shikha Shah
Designation
Assistant Manager
Affiliation
Nuvo AI Pvt. Ltd
Address
135/139 Bilakhia House Muktanand Marg Chala Vapi Pardi Valsad 396191 Gujarat
Valsad GUJARAT 396191 India
Phone
7666070543
Fax
Email
shikha.shah@nuvo.ai
Details of Contact Person Public Query
Name
Ms Shikha Shah
Designation
Assistant Manager
Affiliation
Nuvo AI Pvt. Ltd
Address
135/139 Bilakhia House Muktanand Marg Chala Vapi Pardi Valsad 396191 Gujarat
Valsad GUJARAT 396191 India
Phone
7666070543
Fax
Email
shikha.shah@nuvo.ai
Source of Monetary or Material Support
Nuvo AI Pvt. Ltd
Primary Sponsor
Name
Nuvo AI Pvt. Ltd
Address
135/139 Bilakhia House Muktanand Marg Chala Vapi Pardi Valsad 396191 Gujarat
Type of Sponsor
Other [Artificial Intelligence company]
Details of Secondary Sponsor
Name
Address
NIL
NIL
Countries of Recruitment
India
Sites of Study
No of Sites = 1
Name of Principal
Investigator
Name of Site
Site Address
Phone/Fax/Email
Dr Sanjeev Mahajan
Fortis Hospital
Chandigarh Road Mundian Kalan Ludhiana Punjab 141015 Ludhiana PUNJAB
(1) ICD-10 Condition: T798||Other early complications of trauma, (2) ICD-10 Condition: S920||Fracture of calcaneus, (3) ICD-10 Condition: S921||Fracture of talus, (4) ICD-10 Condition: S923||Fracture of metatarsal bone(s), (5) ICD-10 Condition: S924||Fracture of great toe, (6) ICD-10 Condition: S925||Fracture of lesser toe(s), (7) ICD-10 Condition: S928||Other fracture of foot, except ankle, (8) ICD-10 Condition: S820||Fracture of patella, (9) ICD-10 Condition: S821||Fracture of upper end of tibia, (10) ICD-10 Condition: S822||Fracture of shaft of tibia, (11) ICD-10 Condition: S823||Fracture of lower end of tibia, (12) ICD-10 Condition: S824||Fracture of shaft of fibula, (13) ICD-10 Condition: S825||Fracture of medial malleolus, (14) ICD-10 Condition: S826||Fracture of lateral malleolus, (15) ICD-10 Condition: S828||Other fractures of lower leg, (16) ICD-10 Condition: S720||Fracture of head and neck of femur, (17) ICD-10 Condition: S721||Pertrochanteric fracture, (18) ICD-10 Condition: S722||Subtrochanteric fracture of femur, (19) ICD-10 Condition: S723||Fracture of shaft of femur, (20) ICD-10 Condition: S724||Fracture of lower end of femur, (21) ICD-10 Condition: S728||Other fracture of femur, (22) ICD-10 Condition: S620||Fracture of navicular [scaphoid] bone of wrist, (23) ICD-10 Condition: S621||Fracture of other and unspecifiedcarpal bone(s), (24) ICD-10 Condition: S622||Fracture of first metacarpal bone, (25) ICD-10 Condition: S623||Fracture of other and unspecifiedmetacarpal bone, (26) ICD-10 Condition: S625||Fracture of thumb, (27) ICD-10 Condition: S626||Fracture of other and unspecifiedfinger(s), (28) ICD-10 Condition: S520||Fracture of upper end of ulna, (29) ICD-10 Condition: S521||Fracture of upper end of radius, (30) ICD-10 Condition: S522||Fracture of shaft of ulna, (31) ICD-10 Condition: S523||Fracture of shaft of radius, (32) ICD-10 Condition: S525||Fracture of lower end of radius, (33) ICD-10 Condition: S526||Fracture of lower end of ulna, (34) ICD-10 Condition: S420||Fracture of clavicle, (35) ICD-10 Condition: S421||Fracture of scapula, (36) ICD-10 Condition: S422||Fracture of upper end of humerus, (37) ICD-10 Condition: S422||Fracture of upper end of humerus, (38) ICD-10 Condition: S423||Fracture of shaft of humerus, (39) ICD-10 Condition: S424||Fracture of lower end of humerus, (40) ICD-10 Condition: S429||Fracture of shoulder girdle, partunspecified, (41) ICD-10 Condition: S490||Physeal fracture of upper end of humerus, (42) ICD-10 Condition: S491||Physeal fracture of lower end of humerus, (43) ICD-10 Condition: S498||Other specified injuries of shoulder and upper arm, (44) ICD-10 Condition: S590||Physeal fracture of lower end of ulna, (45) ICD-10 Condition: S591||Physeal fracture of upper end of radius, (46) ICD-10 Condition: S592||Physeal fracture of lower end of radius, (47) ICD-10 Condition: S598||Other specified injuries of elbowand forearm, (48) ICD-10 Condition: S698||Other specified injuries of wrist,hand and finger(s), (49) ICD-10 Condition: S790||Physeal fracture of upper end of femur, (50) ICD-10 Condition: S791||Physeal fracture of lower end of femur, (51) ICD-10 Condition: S798||Other specified injuries of hip and thigh, (52) ICD-10 Condition: S890||Physeal fracture of upper end of tibia, (53) ICD-10 Condition: S891||Physeal fracture of lower end of tibia, (54) ICD-10 Condition: S892||Physeal fracture of upper end of fibula, (55) ICD-10 Condition: S893||Physeal fracture of lower end of fibula, (56) ICD-10 Condition: S898||Other specified injuries of lowerleg, (57) ICD-10 Condition: S990||Physeal fracture of calcaneus, (58) ICD-10 Condition: S991||Physeal fracture of metatarsal, (59) ICD-10 Condition: S992||Physeal fracture of phalanx of toe, (60) ICD-10 Condition: S998||Other specified injuries of ankleand foot, (61) ICD-10 Condition: S390||Injury of muscle, fascia and tendon of abdomen, lower back and pelvis, (62) ICD-10 Condition: S320||Fracture of lumbar vertebra, (63) ICD-10 Condition: S321||Fracture of sacrum, (64) ICD-10 Condition: S322||Fracture of coccyx, (65) ICD-10 Condition: S323||Fracture of ilium, (66) ICD-10 Condition: S324||Fracture of acetabulum, (67) ICD-10 Condition: S325||Fracture of pubis, (68) ICD-10 Condition: S326||Fracture of ischium, (69) ICD-10 Condition: S328||Fracture of other parts of pelvis, (70) ICD-10 Condition: S220||Fracture of thoracic vertebra, (71) ICD-10 Condition: S222||Fracture of sternum, (72) ICD-10 Condition: S223||Fracture of one rib, (73) ICD-10 Condition: S224||Multiple fractures of ribs, (74) ICD-10 Condition: S298||Other specified injuries of thorax, (75) ICD-10 Condition: S120||Fracture of first cervical vertebra, (76) ICD-10 Condition: S121||Fracture of second cervical vertebra, (77) ICD-10 Condition: S122||Fracture of third cervical vertebra, (78) ICD-10 Condition: S123||Fracture of fourth cervical vertebra, (79) ICD-10 Condition: S124||Fracture of fifth cervical vertebra, (80) ICD-10 Condition: S125||Fracture of sixth cervical vertebra, (81) ICD-10 Condition: S126||Fracture of seventh cervical vertebra, (82) ICD-10 Condition: S128||Fracture of other parts of neck, (83) ICD-10 Condition: S198||Other specified injuries of neck, (84) ICD-10 Condition: S020||Fracture of vault of skull, (85) ICD-10 Condition: S021||Fracture of base of skull, (86) ICD-10 Condition: S022||Fracture of nasal bones, (87) ICD-10 Condition: S023||Fracture of orbital floor, (88) ICD-10 Condition: S024||Fracture of malar, maxillary and zygoma bones, (89) ICD-10 Condition: S026||Fracture of mandible, (90) ICD-10 Condition: S028||Fractures of other specified skulland facial bones, (91) ICD-10 Condition: S098||Other specified injuries of head,
Intervention / Comparator Agent
Type
Name
Details
Intervention
NA
NA
Inclusion Criteria
Age From
18.00 Year(s)
Age To
99.00 Year(s)
Gender
Both
Details
Retrospective arm:
1. Patients greater than equal to 18 years who underwent internal fixation of bone fractures in the past 2 years or more.
2. Availability of pre-operative, intra-operative and post-operative imaging (X-ray with/without CT scans) with acceptable quality for fracture classification and size estimation.
3. Complete surgical records detailing the type, size, and configuration of implants (Meril Healthcare Pvt. Ltd.) used.
4. Availability of post-operative and follow-up data including union status, complications, or revision surgery outcomes.
Prospective arm:
1. Patients greater than equal to 18 years presenting with acute fractures of bones requiring internal fixation with nails, screws, or plates.
2. Availability of digital imaging (X-ray ± CT) suitable for annotation and AI processing.
ExclusionCriteria
Details
Retrospective arm:
1. Pathological fractures (e.g., metastatic, cystic, or osteoporotic collapse without trauma).
2. Polytrauma patients with incomplete imaging or missing clinical/surgical records.
3. Previous surgery on the same bone that alters normal anatomy or
complicates implant selection.
4. Inadequate imaging quality for AI analysis or missing calibration
for measurement.
Prospective arm:
1. Pathological fractures or periprosthetic fractures.
2. Pediatric patients (less than 18 years) due to differing implant selection
protocols.
3. Patients with incomplete imaging or poor-quality scans that do not
allow proper annotation.
4. Patients refusing consent for participation in the prospective arm.
Method of Generating Random Sequence
Not Applicable
Method of Concealment
Not Applicable
Blinding/Masking
Not Applicable
Primary Outcome
Outcome
TimePoints
To develop and validate an AI-based decision support model that recommends the optimal type, size, and configuration of nails, screws, and implants for bone fracture fixation using structured imaging and clinical data.
15 months
Secondary Outcome
Outcome
TimePoints
• To evaluate the accuracy and reliability of AI-generated implant recommendations compared to the treating surgeon’s decision.
• To assess clinical outcomes, including fracture union, implant related complications, and the need for revision surgeries, in cases where AI recommendations are applied.
• To measure the efficiency and utility of the AI tool in real-world orthopaedic workflows, including its impact on decision-making time.
• To establish a standardized, data-driven framework for implant selection that can reduce inter-surgeon variability and improve patient outcomes.
15 months
Target Sample Size
Total Sample Size="100000" Sample Size from India="100000" Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials" Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials"
Fracture management, particularly internal fixation using implants such as intramedullary nails, plates, and screws, is a critical aspect of orthopaedic surgery. Selecting the appropriate implant type, size, and configuration is often a complex and subjective process, influenced by fracture type, location, patient age, bone density, and fracture dimensions. Incorrect implant selection can lead to complications, including non-union, malunion, and implant failure. Therefore, this study aims to develop and validate an AI-based decision support system for recommending the most appropriate fixation devices and their sizes (nails, screws, and implants) for orthopedic fracture fixation. The system will utilize structured clinical and radiographic data, including fracture size, location, classification, and relevant patient parameters. The AI model will recommend optimal fixation strategies based on data sourced from patient populations, ensuring that it accounts for both clinical and imaging insights.
The study includes both retrospective and prospective arms for the research, and it involves patient data, including imaging and clinical data. In the retrospective arm, past patient cases are used to train and validate the AI model, while the prospective arm involves enrolling new fracture patients, collecting imaging and clinical data, and utilizing AI for implant recommendations.
Given that the study involves human data both retrospective (past cases) and prospective (new fracture patients), this trial will be conducted on human beings. This is based on the involvement of patient data, both historical (retrospective) and prospective (new patients), which includes human clinical and imaging data for the purposes of AI model training, validation, and outcome assessments.